{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "e68f8db4",
   "metadata": {},
   "outputs": [],
   "source": [
    "import torch\n",
    "import torch.nn.functional as F\n",
    "\n",
    "from PIL import Image\n",
    "\n",
    "import os\n",
    "import json\n",
    "import shutil\n",
    "import numpy as np\n",
    "from matplotlib.colors import LinearSegmentedColormap\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "import torchvision\n",
    "from torchvision import models\n",
    "from torchvision import transforms\n",
    "\n",
    "from captum.attr import IntegratedGradients,DeepLiftShap,DeepLift\n",
    "from captum.attr import GradientShap\n",
    "from captum.attr import LRP\n",
    "from captum.attr import Occlusion\n",
    "from captum.attr import NoiseTunnel\n",
    "from captum.attr import visualization as viz\n",
    "from captum.attr._utils.lrp_rules import EpsilonRule, GammaRule, Alpha1_Beta0_Rule\n",
    "\n",
    "from tqdm import tqdm\n",
    "from model.SupCon import resnet\n",
    "from model.resnet3d import resnet3d\n",
    "import torchvision.transforms as T\n",
    "import data.transforms as MT\n",
    "from torch.utils.data import Dataset, DataLoader"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c482138e",
   "metadata": {},
   "outputs": [],
   "source": [
    "#获取所有阴性和夹层病人的名字\n",
    "ct_train = '/nfs3-p1/zsxm/adpaper/2classification/ct/2d/train'\n",
    "ct_val = '/nfs3-p1/zsxm/adpaper/2classification/ct/2d/val'\n",
    "ct_test = '/nfs3-p1/zsxm/adpaper/2classification/ct/2d/test'\n",
    "patient_cate_list = []\n",
    "for cate in range(2):\n",
    "    patients = set()\n",
    "    for dataset in [ct_train,ct_val,ct_test]:\n",
    "        for img in os.listdir(os.path.join(dataset, str(cate))):\n",
    "            patients.add(img.split('_')[0])\n",
    "    patient_cate_list.append(patients)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "48fa4e38",
   "metadata": {},
   "outputs": [],
   "source": [
    "#将cycle GAN数据集移动到visualize文件夹\n",
    "def select_cate(patient):\n",
    "    l = []\n",
    "    for i, s in enumerate(patient_cate_list):\n",
    "        if patient in s:\n",
    "            l.append(i)\n",
    "    assert len(l) <= 1, f'{patient}:{l}'\n",
    "    return l[0] if len(l) == 1 else None\n",
    "\n",
    "ct_list = [\n",
    "    '/nfs3-p1/zsxm/dataset/gan_aorta_-100_500/trainA',\n",
    "    '/nfs3-p1/zsxm/dataset/gan_aorta_-100_500/testA'\n",
    "]\n",
    "cta_list = [\n",
    "    '/nfs3-p1/zsxm/dataset/gan_aorta_-100_500/trainB',\n",
    "    '/nfs3-p1/zsxm/dataset/gan_aorta_-100_500/testB'\n",
    "]\n",
    "dst_roots = [\n",
    "    '/nfs3-p1/zsxm/adpaper/4visualize/ct',\n",
    "    '/nfs3-p1/zsxm/adpaper/4visualize/cta'\n",
    "]\n",
    "\n",
    "for i, cl in enumerate([ct_list, cta_list]):\n",
    "    for clpath in cl:\n",
    "        for img in os.listdir(clpath):\n",
    "            patient = img.split('_')[0]\n",
    "            cate = select_cate(patient)\n",
    "            if cate is not None:\n",
    "                dst_path = os.path.join(dst_roots[i], str(cate))\n",
    "                os.makedirs(dst_path, exist_ok=True)\n",
    "                shutil.copy(os.path.join(clpath, img), os.path.join(dst_path, img))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "50c980fe",
   "metadata": {},
   "outputs": [],
   "source": [
    "class PairDataset(Dataset):\n",
    "    def __init__(self, ct, cta, transform):\n",
    "        self.transform = transform\n",
    "        self.datas = []\n",
    "        categories = sorted(os.listdir(ct))\n",
    "        for label, cate in enumerate(categories):\n",
    "            ct_path = os.path.join(ct, cate)\n",
    "            cta_path = os.path.join(cta, cate)\n",
    "            for imgcta in sorted(os.listdir(cta_path)):\n",
    "                names = imgcta.split('_')\n",
    "                if len(names) != 4:\n",
    "                    continue\n",
    "                imgct = os.path.join(ct_path, '_'.join(names[:2]+names[3:]))\n",
    "                if os.path.exists(imgct):\n",
    "                    imgcta = os.path.join(cta_path, imgcta)\n",
    "                    self.datas.append( (imgct, imgcta, label) )\n",
    "    \n",
    "    def __len__(self):\n",
    "        return len(self.datas)\n",
    "    \n",
    "    def __getitem__(self, index):\n",
    "        ctimgpath, ctaimgpath, label = self.datas[index]\n",
    "        ctimg = self.transform(Image.open(ctimgpath))\n",
    "        ctaimg = self.transform(Image.open(ctaimgpath))\n",
    "        return ctimg, ctaimg, label\n",
    "\n",
    "transform = T.Compose([\n",
    "    T.Resize(81),\n",
    "    T.CenterCrop(81),\n",
    "    T.ToTensor(),\n",
    "    MT.SobelChannel(3)\n",
    "])\n",
    "    \n",
    "dataset = PairDataset('/nfs3-p1/zsxm/adpaper/4visualize/ct','/nfs3-p1/zsxm/adpaper/4visualize/cta',transform)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "fa97d491",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "14673"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(dataset)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "cf6b34cc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1\n"
     ]
    }
   ],
   "source": [
    "ctimg, ctaimg, label = dataset[10000]\n",
    "plt.subplot(1,2,1)\n",
    "plt.imshow((ctimg[0].unsqueeze(-1).repeat(1,1,3).numpy()*255).astype(np.uint8))\n",
    "plt.axis('off')\n",
    "plt.subplot(1,2,2)\n",
    "plt.imshow((ctaimg[0].unsqueeze(-1).repeat(1,1,3).numpy()*255).astype(np.uint8))\n",
    "plt.axis('off')\n",
    "plt.show()\n",
    "print(label)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "ee3219fd",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ],
   "source": [
    "os.environ['CUDA_VISIBLE_DEVICES'] = '0'\n",
    "device = torch.device('cuda:0')\n",
    "\n",
    "load_model = '.details151/MD/12-13_15:48:33_5最优权重正梯度/Net_best.pth'\n",
    "net = resnet(34, n_channels=2, n_classes=2)\n",
    "net.load_state_dict(torch.load(load_model, map_location=device))\n",
    "net.to(device)\n",
    "net.eval()\n",
    "print()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2d6795a3",
   "metadata": {},
   "outputs": [],
   "source": [
    "gs = DeepLiftShap(net)\n",
    "baseline_dist = (torch.randn(10, 2,81,81) * 0.001).to(device)\n",
    "attributions= gs.attribute(ctimg.unsqueeze(0).to(device), baselines=baseline_dist, target=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "dbc73e56",
   "metadata": {},
   "outputs": [],
   "source": [
    "gs = DeepLift(net)\n",
    "attributions= gs.attribute(ctimg.unsqueeze(0).to(device), target=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "efff199c",
   "metadata": {},
   "outputs": [],
   "source": [
    "ig = IntegratedGradients(net)\n",
    "attributions_ig = ig.attribute(ctimg.unsqueeze(0).to(device), target=1, n_steps=200)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1eb7269a",
   "metadata": {},
   "outputs": [],
   "source": [
    "nt= NoiseTunnel(ig)\n",
    "attributions_ig_nt = nt.attribute(ctimg.unsqueeze(0).to(device), nt_samples=10, nt_type='smoothgrad_sq', target=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "2e4f158b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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hRvn8UVugstoE/4WtJEmSJEmSJEmSJG2JH2wlSZIkSZIkSZIkaUv8YCtJkiRJkiRJkiRJW+IHW0mSJEmSJEmSJEnaknha9AZ0XXflcGU6pDl7+HL2UHQ6cDt7WDzF6Bnj8TjE6EDr7KHU9Nys7MH1dDB39n3pgHY6TLlPnmbLkq6jukHPpd/rk+Y+sgeHU7lRm8keqE71L3twPeUL1T+KZdsv1bXsM+j36N0Wi0WI9WkflBbpeZAdf7sid13bwfgLfw/WddkxD9o+xDB98G51GftEjkE7Tz6X3q2ucv1GBWkhLTyD0rLuYr82qmIfmyvdvGyfSO9B+Uz1qqBnQJmXJYw9VRwvKV+2pYI04zgN5ZudZ9MzNo3KlzyNtEjPoq642v9Sz8n9c66vK2u4m+bU8ARcH9G4CmuN4TD2p4NBvA7X5w2s3woaKyLKKx5T4N3gufSD9L6DAa3BYE3XwJqujQ+hvKrgGVRGtN6qaM2eXOfRuNrQc+EZlH9UblRGgzo3b8I04zoU8hTSQutBzoOYf9l9C8oESl8N9w6T61DKZyojmh+067hX0KyhvuA8m+bK0vNjZ2fnyr4atZHlchli1P5pvKAYPSM7n++z75bd16I0T6fTEOuzf0hpputoz5P6bfo9updi2Xuzedpn/zq7R9ln7ztbdwnt3dK3EnoG7clSjGTrFbWZbIzQddkyojyl/JtMJiFGdQjnK9q44+PjEHv33XdD7MaNG0VRFMV8Pn8i6XC3RJIkSZIkSZIkSZK2xA+2kiRJkiRJkiRJkrQlfrCVJEmSJEmSJEmSpC3xg60kSZIkSZIkSZIkbUk8xXgDFovFlQO16fBlOnA7e9A3HdycPWSdDqWmw7Czh4TTvavVKsTocGhKS/bA9+zB5nSANx2anS0Pkj0snp5L12XzPvt72cPdCZVvVrZ86dBxKnOqQ9nD4um5JHvQfPY6SjOVB+UBtX0qc0pLtl1mUX3JHlIvPWuy/UFZ5K6j3pTuzbaY7HPxOni3YbnZqQ4/N/Zru9XeRp+btVPvhli2zJ+GdRv74gryr+lg/E1WohrKfPAM5UEWzmmK3PjbdjBuQQbWZe73sqgsJX1iuVgUg0fmrh3MMUtalyXXTHQvdX+89osX0hx4UGfX2PFe+r0G1gtFuYwxQOuAmvYFaF2xTg4qlH/QddL8YFDH5+IakZKSLTfIgw7GgBLKl8ZVGis6mO01bW6dV8G/EaggAylfhrBGzNahFtaDlFc0O+hwTybWeyq2Gt6N8rQsIX2UB1B3qe3jHg+8L+VLnz2PltbErpP1HOm67mfu7WT3I0l2/zr7DOwDoQ2fn5+HGPUnffbS6fdoDzD7Hn1iy2WcN9C7UXlMJpMQG4/Hqd/L5l92n5v2Lel9KZ+z3yKoPKgOZffXKU+z3x2yacnuLVM+9/kmlY1lv/nQ3jfF6F56D8p7+j36NpT9LvJFc+/evRD73ve+F2If1YPZbPZE0uGuiiRJkiRJkiRJkiRtiR9sJUmSJEmSJEmSJGlL/GArSZIkSZIkSZIkSVviB1tJkiRJkiRJkiRJ2pJ4GvMGNE1z5fDi7EHVdDBy9oD2PoclZw/X7nNQehbdmz3MPptmimXzmWJ00Dw9gw7hzh7+TemjGOVBNpatV9m8yj6DYiR70HyfQ+CzeZX9Paob2TLPti1KH6HD57N9TvbA92y9/6KhejCdTkNsPB4XRfHHffKDBw+eeLr02bUd1XuIwb1NAWMjtBnqEbGdYx/2xfo7NJyXdHFOU5dPZLr3WEb1OHVd1cFY+wUrX5Jtb4TaL6nLmPdZ2bmF9EXVtm3RtJ/00+U6N/emuVSZXH9wL0FrxHhV00C/UcZxpsL5ATyjTa5rIUa/F1PyU34P1vsdrZ0hX+oK1gaU97RuHMC98HZrSh+te2idR3OpmuoGrENLKA8YaqluUFlWtE5OjgHpe2ndWMXrqO6WUE8pr+jdKO+LAtIM70EZSHtQA2rn2X0pLKRce6P1L/U5dF2B6cv1B4/2hV9UgzrO0T9aEz9q+Mi+xWh0+UTT9EWzXq+vtLNsm8vOb/vMg+le2tci1Mdk9zKzv4ftGq4j2f11+r3sHvRyuQyx7LcIivXZz82WZXavNbs/vFqtHvs6kt0bXSwWIZb9tkF5QHmV/d5BdYjqBsWye+TZfW56D7puPp+H2OVl7Pspzdm2KkZt4fT0NMQ+/PDDoii4rm+CO1+SJEmSJEmSJEmStCV+sJUkSZIkSZIkSZKkLfGDrSRJkiRJkiRJkiRtiR9sJUmSJEmSJEmSJGlLcqeV90QHHmcPZCbZw7UpRgct04HC2YPc6bBpOiA7e7h7n8PsKV/o3uwh8H0OVKdnZGPZZ1CeZmXLLZun2fIl2QPB+8QoLfQe2YPIs/mSbUeUvmx9yeYBpWUymYQYvVs2RnlA/dDn2Xg8DrH9/f0QOzw8DLHd3d2iKIpiuVwWDx482Hzi1FtZwHhUJvts6l7o5+D3Koh1xeOPl58XOB6VT2Vq98RRmasoBtUwxJbNIsQo/5oO5uNdbj7ewr3UVqld1sXjz9ekz7smOZ+sWmiD8Hs0B26b+Axqq3Rvs4Z+o8m16V5rJri3zxjfQT53NfR1kFekbKCMKM1QSrSGwDUO1Y3kWq2Cd0ujNR2tk2nfAtMCZd5jfwPXfi3kS/I6Sl8JVbylfQsYB3Gfi9rCID6E2mVVjUKsaWB9iXUX8iVZviNY01GdxHUy1g3Yl1rk9qU+L4aDOIfb+ZP176P2IDadTj/+v89HsWz0+Lquu1KPs/ug2Vh23zK7F5d9Ls4lerwHpW+5XIZYdp+szz43jZnZ/Vzan6NvAnRvdq8/m/fZ/VJC5TEaxfGC3pfejcoyu9dKv0cor2j/kPZp6d0ozYTKl2Tb4HAY+/I+33IofZeXl6lY9ntb9t2U768+alvZb2yflbthkiRJkiRJkiRJkrQlfrCVJEmSJEmSJEmSpC3xg60kSZIkSZIkSZIkbYkfbCVJkiRJkiRJkiRpSx7/hOtPUVXVlQO16QBeimUPzc4ekJ19BskeJk6HTWcPGM8elJ49QD57uHv2QPU+B8PTM+heOmS9Tx6Q7OHflBbKF0pLNn2UluwB6NmD3LMHXmfrULb9ZsuSruuD0pxtq9l3y7ZpFcXh4WGI3bhxI8T29vZC7KP+frFYbD5hemLKItcXV9Vm2352DCBtB317+fy1aXqProjjTF3GPlH9zJtZiOF8A+6dDHY2mxiar9GTIX1NEce8VQvzgy5eV1G9grRY/54t2bXQo31JB32NHk9ZVUVVfdIm2ja2rfUa+nYot0Ed17/ZtQbNn3M1g+fAdR1jgzqO+yXNn6nvpBg+F9aNyTVY28Q8WK/X8RldfAb2sfRcyNVmnVtrZPcjeM3++HMaGreqAeRzS2UEa6bknkKfdXIDYxTNkaje0+yU3qOlMRTWjes21iFuC7QfEfO5gf6g7SCfIUbdfQXPrWkdD++GIwGtk5PjTPEFW/LR+vfg4CDEpjtxnjgcflJG1E/p8XVd9zPnRjTu0f5Sdi8zuz+cjVGdyPaztD/XZ38zu7ec3T+k983uN9Pv9ZknZesBXUfvSzFKX3buTrL7+vQeOFeEtGT7pOx+M33fyeZLNi3ZMurz3YFi2TZNe6J9vq2JUT2YTCYhRmP1R9f12Zf8NM/f7qQkSZIkSZIkSZIkfU74wVaSJEmSJEmSJEmStsQPtpIkSZIkSZIkSZK0JX6wlSRJkiRJkiRJkqQtiScqb8BwOLxycG/2oGU6gJoOWqZDpOleQgcK00HkJHugNV2XPQCd3iP7btnnkuxB8/QMOhCc8oUOMafn0nWkT5lnr6P8exqHrNNz6d7VapW6bjwehxjlc5+2kM0ret9sm6ZnZMs3W6+yB7RTmmezWerez7NsO39SB7Pryfq8lFtV5sa3Z+l9KX1dEWN1mevHlXe2PA6x75/8mxAb13Gs3R3shdjr+18JsT7lNihjH0t1Y1CNQmzdxjkcqSuYJ8LffeZGUPast8HPC6obpO1a/L/Vz6Cui8Ej8+0VzjtjfndQBrQOwDV2cm5b9Zhndy3M+YuYFmrnNHcs6bm0roD5eHxqv/6lTq+TYZ23hnVjE9M8qOMz6Lk0pyYt1ANSVjEPyoLqQRyjuhLmJZAH2XUyrhtpb6SLv3e2PIvX0Rq7iO9xOD0KsSGVOdUXuK5dUX8J93Yx1jS5vQLqxrN7N9Qd0PviQ5KDPKYZ9vW+aAbJdTLVte6n/N/qb7VaXWkrVH+zfW92T5Fi2WfQHjntQ9He3mgU1wLZ9GX3tLOye/OUZnrfbN7TPuN8Pk/FKC303MlkEmLZ/Vea22XfLfudJZsWimW/CWTTTPWZ0kxpoffo870oW5+z+Ux5lU1Ldv+a8g/HYKHpdBpid+7cCbGXXnopxD5q5336wU/jv7CVJEmSJEmSJEmSpC3xg60kSZIkSZIkSZIkbYkfbCVJkiRJkiRJkiRpS/xgK0mSJEmSJEmSJElbkjvV/DPa2dm5cmD66elpuIYOgqYDqOmg6uzB0tmDoEn28O/sYdh02PTe3l4qLSR7CHz2XjrgnmLZg8PH43Hq3uzhzJR/FCPZfKHf63OYOKHf61NulKd90kzlQc+gGKFnZOsutTe6jp5B92bLN1vHCT2D2hE9N1uHnnVU17J9zkd5n61f0iZl2/m2NB30GwW0twLGitI2lfXh7L0QmzfzEPvg8m6IDao4hn7r2rc3k7BPkS5fqOKLZhZik8FO6ueaNs69110cf4sW5nAFzA+o7hYxT+m6Z739PkuaLpYbzUC67pP+he7R45lOp8XOzidt7PziIlzT0DqZ2gfNWat4XbOO40cD87WW5qJwHT13TXNbmI/TvVT/ptMpRB9fnZxblrhehXymGPxeBePCcDSK19E6L73+yO1v9EHrlJbWnFSHeqyTV90SnhHfd9Es4nVQd6dVrFdNG5876GK51bR2CZHPUm40NsbnDmrY+4K2RRWQ8or7gxij/TXaK0ivk6Gl19A+cN8Ce4nnD7WZroT5EPUvJf/f6u/y8vJK3ab9ZtrfpP0MaiMj6PPPzs5CbLHI9WMUy7ZXupeeSzH6vUfnM592HfXv2b04yvvJZBJi1Bdln5vdZ8zqs19P9YrqUPa7A5UlzbGobmT3Rqn+0XPp95bLOM7TdZQ+KkuqG33267Oy9Y/eN9t+6T3oXpyzJb+FfdHcuXMnxF5//fUQ29/fD7EnvX/tDp4kSZIkSZIkSZIkbYkfbCVJkiRJkiRJkiRpS/xgK0mSJEmSJEmSJElb4gdbSZIkSZIkSZIkSdqSeJr1BpRleeXA5ezh2tkD1elAXzpUOXuYePawePq97MHXhA5Pp3ejGKWPYvRu2TylGB12nj1QPZtXVJbZ98jGsugAb5I9jJ1i2cPEs+U2HA5T12VjlOZse6PrsnU3+9xsPmcPcifZcqO839nZCbHz8/MQo3d7Hs1msxBbLpchRnn6pA9tl54X2f6gaWO/1pXx3lEd5xtZbRfHnnW7CrGT5YMQmwxi/1fAu52vz+CyeN3ucD/EBmWcH6y7mL7D0fX43NVpiM2beYg9hHc7X8V+/Nr4KMS6AsatIjcvfhrG9eSJP6OFPOigXpUlzEHg3gquy8rOIz7PKvh73aZ4/Lmy+snWvk2vk1taJ1cwp67j3JbmwLjO67FOHsGc+tPmjlfSB+tGXk/HWNtAnmLfFNMyHNJ6BtZbEMMygljXxHG/gvJYwFiGYw8UR9PB+hemJdCN/5R1WbyO5iUtJQbuXUP6KDaoaI8nxgZVbh1aJdecNL5RvaKZHqWvSvYS9Fzq73GdDPUK18k4Xuba5QDebTyJcxBaS9Lc8Xm0WCxCbL2KeV/C9PnRPKW+Wo9vtVpdGZto34jGuPSYBG2JnrFaxfVLdt6aHvuT70Fo/KZ9nmxasnmV3dfPzi/67K9TjPbIaV+Q9rQpLfSdYAJ9Jd1L5TudTkOM0kz9U7ZOZvOKyojeY29vL8Sye62Xl5eptNBzqV1SmrOx7PeE7LcSuo7yJbsfnm0zzyPKgxdffDHEXnnllRC7du1aiFF9ofaxSe6KS5IkSZIkSZIkSdKW+MFWkiRJkiRJkiRJkrbED7aSJEmSJEmSJEmStCV+sJUkSZIkSZIkSZKkLYmnXm9A27ZXDi+mw34plj1omQ6Mzh6oTgcFbzqWPUCeDminw7/pfbMHw/e5l9D79ilfilFZ0gHt2WdkD/rOHsydTQvlPaHrsoe703OpDmVR/mWvo/RRjGTzKntIPRmPx6nnZg9ep3ej36ND4Kl8F4tFiGXL41lycXERYicnJyE2mUxCbHd3tyiKfJuVnhVNG+tsrvcriqqMfVjXQR9bwJymzPWda0jfoIpj2bKJ/dC8mYVY28V+8p3Ln4TYtJ6G2KqN/d/OYDf1jOlgJ8TKKvax4yo+l+wND0JsWMUx9Nr4RogdDA9DbFLHfu3m5IUQozJ/GqgeLNtY5gPIU1JDHSo6mEtBmXfQQjooc2oLTQlrA2gL9NwWfo/KnNrH5wX1GyX8DW/7SJ9Tl5/f/Hja2q4r2kfmkOl1VHatS+vkn5KOcC9MOytKH60Hs+tkmGdn1840py4gfTiGQiZU2M7h9zAHaV1G+ULlC+M+5UuT6zuH0GfP2zh2F028dwV5X1cwt6D8K2DdM4hpGdQxRnVjWMS+eAjbFvUa+jC4cFDH8p0M4vyAyoNQm6GK1bUQo9+D9ktJqYdxDUvajvZpUrdi26Iy4rUpPISyqoQ2U9P+S6z3qxXMs6GdP+vm83mIXZzHtTPt50ymn9TdFtqyHl9Zlj9zj5TqPu3nZvdaaYyjsTC750Sy+1rZfe7sfml2XpPNK0oLvVv22wH1d9k9XpLdC87mM6WPfi/7/aTPcwk9g/KA6i7Ve3qPPuWWvY72I2ezOHeifpveI7uH36e90XX0boTSku0jPi92duJe0kd70I+aTuNc8dO+xzyp/Wv/ha0kSZIkSZIkSZIkbYkfbCVJkiRJkiRJkiRpS/xgK0mSJEmSJEmSJElb4gdbSZIkSZIkSZIkSdqS3OnEPY1GoxDLHtpO+hzqTQcFb/qg9OyB29nDtbMHoNN79DnQOnuIOb1bn/Lo81y6N3sAdPZwd3qP7HWUPnqPbCx7yHr2gPFNH5ROsu9GeZq9l+rzeDwOMTpMnMooWycp/6iMqK+j38v2ic8SyoP79++HGL3viy++WBTF8/neev5h/1JA/1dCX0fzEvibuLaIfQn9XryqKIblk5+yDao4fgy62KZX0CdO6kmI1WXsx3dGuyFGfTYZVnE+uTPYS92bNYb3oBilha57Gu7N3w+xtoOxDGKkT55SmRclzL07mHsXMBft4liB1xXxugG0mXURn9tBi6O8wrb/HMK1C+Tpo7GKylUbQfP2FtersS8mVL44b0+uV2m+VlZUXzAxqetwTUfz++QajIaUpoltmuas+HswnlObobfrGphbVDEtVG6DKvZhqyamuVnHMqqL+B7U71YdrM/b3LpsWMa6OxrE9daA8hTqUNFB3aX5FcxVRlVM83gYx2Ral3U0XkJ9ya5P1h1ch+0Nxpk2lsewi/ONGt6jgD0PXGNDf077dTg/Ta6Tqf0OBvG5S2iDa6jP7dl5vA7K6Fm3bmIZnZyehhjl6Y0bNz7+vxez+WYT9gVX1/XP3Mvqs8+Y3QOk38vuQxEeH3P7viS7n5vdK8zGqH+i8Tu7T0u/R9dl9/twnpT8TkCy9YqeS/fSuEfvm60v2fpH+dwnT/vU+8kkzgd2d+OexMnJSYhl236fPffs/jChek9riz7fx+i659HFxUWIzedxPKUy/7Q8zbbZz+rzsfMgSZIkSZIkSZIkSc8hP9hKkiRJkiRJkiRJ0pb4wVaSJEmSJEmSJEmStsQPtpIkSZIkSZIkSZK0JfH06Q0Yj8dXDrbOHsJNh0P/rMPfP+1eOhSYDnOezWYhlj2smw5fpgOKswd40zPG43GIUb7Qu1H6sgeMk8ViEWKUV9nD4rMHoJNs3SB9Dp8n2cPEqW5QPaB3yx5Sn21bJHu4O9WrbPooD7IHpVNdo9/Lpo+emy03uo7aB8UoLdn8ex5R3/TgwYMQ++jAd8pvPb6ua4u2+yRPyyLWta6IbaYqv1h/07XuoE038xB7NC8/7d5hFceUpov9aV3C/ADKaFRP4r3wd3d19fhTu3Ub34NQHtzZeSXEDkfXHjstT8O6jWNKgW0hltEYyuNpuFxfhNgPjr8fYkfjoxB7be9LIbY3PNhIuj5NW8T6smxhvIT2Ru1jZ7AXYlR3VxCj3xuUNBd9Ikuk58qj85LP8xzlaRsOhlfmuNk5el3T2iC7To7XdbAGy85tmxbGsgreA+bjNEfnNV28rqjgXsq+CubZcNm6gzUs5GkHz8V+bRnzqm1ya78hrEOpeKm+dJCW6XAa0wJ5ui4efx1fQb0ilAftOsaoXhW4hxIvGw5i+giuk2HeRPnSwPpkuY5lfrY6DbHpKJbHtN5JpaWDOVddx3Uy5B7vVcHvUawr472rJvYRFcxFsS9ZxjF+tcrNw7DQPyfW65hXp6exDj26nr68vHyiafqi6boO28qfvuZPo70pkt37oT2QbIzSQvvIFCPZfUHas6Pxgq7L7nnSvRTrswdInsYeb5/9yOw3EEpLNq9ovz4r+x2D6i7lAe0pkskkrtEp/yh9lJbz8/MQy34Hot+jGNW17L55NkaovhwfH4fYz+ofn0WUZtqD/vDDD0NsZyfOzw4O4p5Jdg33uL5Yu7GSJEmSJEmSJEmS9Azxg60kSZIkSZIkSZIkbYkfbCVJkiRJkiRJkiRpS/xgK0mSJEmSJEmSJElb8vgnSH+K5XJ55YDkxWIRrqEYHbRMh1xnDzymg5bpUOXsYeJ0L6Uve6h89uD67CHm9Hv0bnQwcvbA9+wh8JSWbIzQdXSYffagdMrTbP5lr6NnUIzQe5BsHaLyzdar7EHzfQ47p+dSnlIs+1x6X+qHLi8vf2o6H5Wtz9m69kVDZXlxcVEUhfmzDWWR63M+L9oO2iVcNyhj/9eU8d6qi33OoIpj4wjymfK+g9TUZRy7Cb1bVcb0rds4z1l3cfyoIQ/w3apxKn1PA73bB7N3Q2zWzEKM8vlodC3Ero1vPmbq+vmj0x+E2D97/3dC7GtHr4fY3nA/xCb1NMSofPsYlPH36jKWEbW3YR3r1bCK878Cpld1HX+P2gL5ovWJenrWzapYrz+p/6tlbAvL5TLEWpgb1TDfzc6gSrh3AGu6JcyV16s4Vgwmsb3RmrOg9SWkD+fZTW7sruEXWxprW3gGxDjNkGq4rh5AHsAj2jbOi3EtTvMI6NdGw9h3dpCUOrn3QPncJsujaeIzLldxvdW0UK8GsU4OYYzCcYHW7PS+Va5uVFAei3YeYifLs5gWyPvRIKaZ3o3Kt0vuM/DaHp7Rwrst47st57FvqmDe1MGgTNeVRaz31CN80UZkKsvZ/JPymEO/rMe3Xq+v7B/R2EV7YhSjfSga02kvczKZ/My0FkV+z5jSR3u39L7zeWz/sxmsm+Beet+dnZ0QG4/jOJXd/8++B6H8y+7tZfcoKUaye630vpR/VP+ye9qE7qX8o+to/MmWEbUPel/Kl4/2FB9F33ey358ODg5CjPIv+42Gyoiee3p6+tj3Uj5n62T2W8TziPqmN998M8So3L70pS+F2HQ6/am/uwn+C1tJkiRJkiRJkiRJ2hI/2EqSJEmSJEmSJEnSlvjBVpIkSZIkSZIkSZK2xA+2kiRJkiRJkiRJkrQluROuP6Ozs7MrhyFnD0XPyh5enT0sPnuo8u7ubojRoc90oDW9L91LebVYLEKM3iN72Dk9g/Ige3g1HQhO70sHN2fTkpU9fJ7SR2nJ1t3sIfV0L5URpZnqC8nmc/ag+ezh6dlYVp+D6ymvsgfDU4zSki1Lim06759H9B6UV+qvLKuiKp/vv8/C/qWANl3kxo+2y81BSsi3cRnHGfq9UT1OPYPQ71EZNm3s26sq9omk6aCPTebftI7zoUH1RKaUP9OyXYbY5eosxD6YvR9i6zbmwaKJc66HwwfxGeuLELs5eSHExvUkxLLm68vUdV85ei3EzlYxfXdnH4TYsIr1+c7Oq6nnZtFYtjOIdaiANl2VsT5T26f2QffWEJOepsuLyytVPTvHRLSuhctoXkzz5wJi2VXZeBL7ujq51m3gfcsK5uM0917BPJuaeQdzashmjJUwB4EYpXmYXCe3MCY36xjDsoRxn/q/gkLwey3N0WmdTOsemJesm7g3smjm8Tp43xLG5NEw5ul0EPN0WMV5WAn1oG1gbUV5QPMmKPPd4TTEVk3Ml/k65gEMb8V0uANpidcVMNZWNYyNUF/KmLxiSeMqrbHb3J5HC9d18CLZuShdR/OD5xG9R/fInLXPXqqitm2vrDWz+33ZvST6PdrjzaL95uxeHD03u4e6sxP7InoG3Uv5Qs8dj2O/nd27pf3r7J52dl+a3ne5jGtRmttNYJ5EeUr5Qu9Bsvv/83kcf7LfTyh9NDZQrE/fld0Pp/egvKf3oOv29/dTsbt374bYyclJiF1e5tb3fb75ZPdaHUu4PN56660Qo77zlVdeKYqC29MmPN87uJIkSZIkSZIkSZL0HPODrSRJkiRJkiRJkiRtiR9sJUmSJEmSJEmSJGlL/GArSZIkSZIkSZIkSVuSO7n6M1osFlcO6M4ext7nEGk6MJpi9Fw62JzQgeD0e3QIPMXogGeK0SHm2cOh6bBpOnQ8i/I+e6h8Ng+y9YAOzaa0ZH+PUP5RLFuf6d4+acleR/mcbZcUo9/LHoBOKK/ouXTwOpV5toyyB81TG5zNZqnrsvU+m/d96pD0tKT7qyJeV5WxP2g6GregzcBVVRn7CDKqRqnrngbKA1JXjz+NW7fQN3Wxz6b8a8vYhw16pCXr/vxuiM2b2Be3XUwf1clRHccAyheqG797/3dDbG+4G2IHo4MQqyFPv7T/1RCb1nHe+a1r3wmxrx5+M8T+xb3fCrH78/shdnf2YYjNjmKevgHpy9bTrOzoRvlHMelZtFytivqR+SKNW33WyXQdzp8hViXnyjR20/yZ0HPX69jvdi30CLD8bdZx3EpPleHCJrlOpnIb0jqU5vdQRqtVbr2P8yb4vfQam+pavKqgFd2qWYTYsollCY8o6gr6bAi1UOgVpPB4cRxigzLWtdEgjudU13ZHezF5MM4cjI7ivYN47/HiYYjNlnGsvSwu4zPaWCd34Bl1HfOF8jnbPKjt083tOpbRYgH7AtBW17BX0MEcjvpErFiuk/UYBoPBlf6R9ktpX4uuy+5rZffsSHavi2T3UGmPnOYS9HvZPdQ+70HX0XtQmrNpITTX2d/fD7GDg7j+o+8Jo1Fu/yG7h99nHzSbf1THs/u+2XqP859kuWW/CWT3lrPzb9oLpnKj/WFKC9U1uje7Zsh+o8F5f4/+6llHdff8/DzEqCzPzs5+6m9sgv/CVpIkSZIkSZIkSZK2xA+2kiRJkiRJkiRJkrQlfrCVJEmSJEmSJEmSpC3xg60kSZIkSZIkSZIkbUk82XgDRqPRlUOO6XDjPgcj04HMdCh69iDt7OHV9FySPbA8e3B99sDtPofZ03Pp9+hQ9Owh4WTTB2RTntK92Tyg6yh9VK8o1ke2nlKas/lMdTybf3TIOpVHNk+prmUPgc8+g94tW5bZ+jefz1PPzZYHPZdk+yGpKIqi7WCcKXJ9DukKGBfgGU0X+91BReNMvG7Vxj5n0S5CrC5j+x1Vcc7QFjE2KGMbrMrn72/dcKzooH+GcisKmIeVcX61aX/v7f9XiDVQh3YG0xD7yy/9uyH26t6XHjst712+HWK/8+G/DLEfn/4kxK5NDkLsa4dfDbG9Ybwua1LHPPi3xz8Msf/6D/9FiFGe/o2vH4fYlw++/niJ+wx2B3tP/BnStg0Hg2L0yBqV5oSkrHLraZqz0pq4quNYxnNlmmfH8YP+DJzGma6CNWyde7emoblF/L2K5irZdTLdS+vkeFUxzK7V6Bk4v8qt9ym2hvUC3gv1qmvjc987fzc+A9Y9o0Gsa7d2XgixitIMv0cWzSzETpanIXYJ1w3XcV63N4xjz6CifaRc+6ia+G4Xq8sQe+/sQYjRGvbOfpzbvnEU01xXse2X0KYLWidDvzGCujGEfKG1KZVlA2ti2j+gtpXdf2nW2XWK62R9Yjgc/sy9u/QamMYkaodJ1CfQnhM9N7s3SrJtjtC+KsWy+2m0L0j7/3QdxbKof6Ln7u/vh9jeXuyjs/uW2X3V7P4hXTedxrVjtp5mv0VQmjeN6mS2jme/WdB1NK8+OIhr+WxboBjl32IR5wMk++0gO6fM5unnBeX9xcVFKrZJz9+uoyRJkiRJkiRJkiR9TvjBVpIkSZIkSZIkSZK2xA+2kiRJkiRJkiRJkrQlfrCVJEmSJEmSJEmSpC2JJ1JvQNd1Vw40pgOKswdkU4zQoc/Zg7TpQGE6AJ3QIdfZA9qzB0bTvXR4evYZdGg2HUBNsodXU1qyB4JTmVN5ZPOvz6Hj2TpJsT7PzR74ToeTz+fz1L3ZOtTngPFs+6A6RO0ym1cUo3vpGZSWyWQSYvRuhOrucrkMMcrnbHlQmum52XYuFUVRNB20QYjVZaynbQfjTBHrXwl/N9a0sV9bt7HNZGvzuo1toS5jn13T+0L6qvL5+1u389VJiC3aOFacr85DjPqc66MbqeeO69h3knvzD0LszZO343Wz4xD7i6/8auoZfbwwfTnErk+uhdjlKubptfFRiL2+/6WNpOvTUF79y99/M8RoHvHG4VGI/cWX4r2v7j3595A+b7ri6vhFs+yygrUzrq3ieFTCL1ZwHc1tSxjf2jaOjctVjJGqonlsnB/UcN16FfumuojXDSBfaF+AxrIlrfdhrhxTzHCdTNcVMJcaxvfoWlhjD2Ed1cQUzldx3lTBfK2ifGnjGvtscRFiDeTM7UmcH2B5hAijPN2pd0Ns2sT5y+VyFn8Qqi6t43HNCXkFWYqWTaxrF5cxnxsoy9MRvVuMDQZHIUbJK2mfgdbdkBaqz6Nxbk+BNDAHwX04XCdT/xfLktJMz6V1ir4YyrK8MkZk9/ay+2TUj9G9tDdF8/TsPk92zz27P0d7WJS+7J4dvS/F6N6dnZ0QG4/HIUbjD/0elQflc3bvkfKP9q+pPChPKV+y30qy9aBPncymOTs2ZNsblXn2ewelL/vcbB3P7udm21F235fu3fR3ID15z9+uoyRJkiRJkiRJkiR9TvjBVpIkSZIkSZIkSZK2xA+2kiRJkiRJkiRJkrQlfrCVJEmSJEmSJEmSpC3JnVL9GdV1feXA5ewh19kDqOm67OHfdOh49veyh3rT4dXZQ+CzB9fTgdF0Lz1jNBqFGL1b9kD67IHWhH6PZA/1JtnyzR6Knj0knFCe0rvRweFZdPA6vUe2PmfTR9dlD7PPHmLep81k+wiqB9n6TG1rMpmEGNUXei6hPoximz7MfrFYhFi2r9Ozhcqo63J1vI91G/uNs9VJiNVl7K/Wbay7P7n4SYg9XDwIsdvT2yF2c3IrxI5G10KsKeNzy2Ia09fBdWXsXyifx3XsI/o4X52G2GVzEWJNC30dpG9ax/dtC5jTwPtm3Zy8EGI/f/PnQqyBevAX7/zVx35uFr3bv/PyXwuxdy5+HGI3J7H+XR/H+tdH08WyXDWxvU2ncYx6863YZu7sXQ+xg9HhY6bu2Uf1nnRQ77OoX9MXU1VVV+afNF+r69ycFX8f7uX1TLy3q2guCv39IN686OI8cdUsQ2xUwZy1i+mj9lZWsIYIEZ4zVBXkAWTpsI79JOUf5imU0Wod82UNfTbOn6sYa9bx3qqAdV5Na7BcvdotYxldb4/g92J53BjfDLEmuU6uYd24hjVn08Q++2gQ53CTMs6vRlC+k0Gc51BaSAtroRW877qJscEgltsKynd3vBNiVE+r5JqY0L1tcs3etbmxkdarI9i3oPVqh+vV+AwqN8rnqoplTm2rxTVTjK2Wsa/DNXaPeYSenOVyeaUv7LPXStdRLLuHmt1HpvZFe3G0X9VnL4n2iGh8pD0xSgu9G+VB9t2y+5F0XTZPqdyW0CfQvTs7sX/HPia5/5rdr6dy61Ofs3ut2baQfQblKeUf7Vtm6wHtr2fbB9UhqhsPHsT1eHafm+oGPYPyIPu9KFv/tFn+C1tJkiRJkiRJkiRJ2hI/2EqSJEmSJEmSJEnSlvjBVpIkSZIkSZIkSZK2xA+2kiRJkiRJkiRJkrQl8UTlDei67spBz3S4cZ+DoOlgbjocOnvAePaQ6+wB43TYdPZAZsoX0ifNdGg2HQxPsewh5n0O9abnZu8ldG+fd8vGsoeEZ6+jukHvQflC70a/ly3L7AHy2TKiNpPtI7JthmTrGuULPZcOld/d3Q2xxWIRYtl3o2dMp9MQy9bnbNun9NFh9vRul5eXqXv1dFAdrwqoByWMeV2sQ1UJdQ2uI4eja6nr6PcGs/dC7Gh8FGIHo0OIxesmgzi3qEroI4qYf2UXYx2ked2uQozyeVA9/vRsWI1CjNI8rifxOqgbTRfbfg35Ml9DO69iO98d7IUYOVmchNgPT34cYr9869fiM4b7qWf00bRx3Lo9eTHE2iLXFvp49+KtELsxjW3r137uKyF2eBjL4xdufiPEVu0Xq8+m9tsVNN/IzeGylxHq/6hNU12rkn+bm52LagO67o//38f/E+a7EOvqXP0bDeIYMKjj3HEwiDGqpzQGVDWM+1T/urjmLJt4L40pbZntO2E90+TWBlUFaxeYZw+S68YSYg2kpYF1D82lqF1iLaD2i0063l3RnBDetyvjvWfL8xA7hPkV9kO0Po9X5dfJ8G7TYW59RGWO62RYC7Vw3aqLa6GdcUzLdXi181G892AUx2magzQttNX4iLSa9tLgGV0LeQX5Qu1oOolz0eUS1snwXFIP4jNw76uCPmeYWyeX0G+0kAerVZw3rZZxHTCfz0Ns3cTy1ZOzs7ODZf0o2q/K7mFl9+L67OfSHlF2z5Peg2T3Vem67D7Z/n5cwx0exrX8aBTnOiS7T0v5Qmmm36P9r7292G9TjPJvtYr9RHb/tc/3BNLn+0mfep+NZdOSTV8WlSW9G/Xv2X1VqhsffPBB6t4+fdPR0VHquouLi1Ra9Pj8F7aSJEmSJEmSJEmStCV+sJUkSZIkSZIkSZKkLfGDrSRJkiRJkiRJkiRtiR9sJUmSJEmSJEmSJGlL4gnmGzAcDq8ckEyHZtPh0HT4MplMJqnr6FBlOnyZDgmnA8b7PIPeje6l6/ochk2H1FOMnkGHXNO9VL70HtmDvjd96DjFsgduZ+8l2fKl96B8zh7QTuVGsm2QrqP0kT55StdR26JnZOsVPTcby+b9eDxOxagd0fvSc6m/orRQvoxGo1T66Ll0qPzZ2VmI0Xt4IP2zD9tCEWPLNpZl18X2e746DbEV3Duu4xhPz63KWJ+ng2mIXR/fCLG6jO0Dx/MutsussohtZlzH9A2qzU7FKP9uT1/a6DPIbH0ZYpR/Z8vjEGuLWF/+/o9/M8T2oL/6px/84xD7a6/89Z+WzI05GB098Wdk3V/cC7EK+mzyH3z7F0Lstf3XUve20M5n64sQW3dxDPjd+78D985C7Jdv/WqI3ZjcTqUvi/qSJaS5oHldB/N76K+oNOpk2z9bncDvwRwY0kJ9zqiK7agun8iSUGAwHBaDR+ZtNEdqaV68itdRXRuO4pxwBH3sqonjLz13Dc+lOWZZwToP/ja86mKsgzk/WeM6ObdGJMNBbAu4Tk7O+bNr9nEHexnp+T3lX65voj57CfWghOK4e/kgxCroh+7P74fYrcmt+IPUn0JbIAPI+zq59suuYSmvsF1C3V0Vce5D6aM69ML+QYiN67guw32GdSzzVRnTQs3jbA3rtyaWx8HwMMSGZayn1GY6yIMhzOtGo/i+S1gnd8m9wwraTFXFvB/B+pfqCw2XNCavVjFf5rO470j5TDE9OdPp9EpZU9vM7k1l9/vo9+i5tFdDsez+V3Y/nPZq6BnZffM++4w7OzshRnmQ3Z/L5hXdS+9B+Xd+fh5ilFeUB9n9zey3CEL5nK332b15el/a88yivO+zP4xz2WRbpeuy37OoPt+5cyfEsnvG1FYpzVRPs/lH7a1P3pPstyt6t8vLuA/1edhv9l/YSpIkSZIkSZIkSdKW+MFWkiRJkiRJkiRJkrbED7aSJEmSJEmSJEmStCV+sJUkSZIkSZIkSZKkLYkn+G7A/v7+lcOB6bBfOvA4exB59qBvOgia0O/1OWg+G6NDwukZdF32MGc6pDmbL3Rd9vf6HORO92brQfa6bJ72eTeSrWuUz5Rm+j06iDx70Hf2fQnlfRblQR/Z8qC+ifJ0OByGWDbNdB0dIH9xcZFKS7YOZdNMda1P+6XrsuWhZ0tXxD6ngH4oa1DFOonPAHUZ6+mdnZdCbFxPQ+xodD31DPVzvLwfYj88/WGITetJiK27OEf6lz95N8SoL7mAfvyNgzdC7KsH3wyxz7Nb01jv/ze/+B+H2PHyJMQm0I6KIub95fo8xP7bd/9BiD1YPAyx//z3fyPEjmBs3P+l/RD7ldtHITasRiHWB41aVZkb91dtrJOP33MWRVXCPIzmsZA+x99nz3Q6LXZ2dj7+3w2sEXF+CvPstoW1EMwT6ffqLtaXrqNnxLpWVzC3rWL7pXtbaA1NG/OA6nhJLROWH9UA5rt1nIMM6h5bIdl1cpVcS8IjqprW+/HdKP+WTdwbOVvGPruAMqI15/3TeC+9xxrq5O5wJ8R2BnuQFOrXYP2RXCdTW6A9GSojgmVE62SokwOYx768eyvE1lCWw0Ec38o2pqbp4vs+WDyIySvjde+eHcfrIK9e3YuxW7u3Q4z6CFLBM7L7TbT3QDG6dziMz6ihPxjAWrcrk2sX+L2yjH2x4/T2jUajK3sn2TIZjaBtJscGkt2Xye7PZfdL++xLZ/OA7iXZ/SX6vew+VPb3smmm62ismc1mIUZlTnuFfWT7zz75R+VGMdqrzuZ9dq+f0N5on3aU/eZDeUXlm/3+dPPmzRCjb2GUFloLUIzuze5BTyZxn2d3dzd1b/Z7FpnP56nYyUnc96Dr+nzb2CT/ha0kSZIkSZIkSZIkbYkfbCVJkiRJkiRJkiRpS/xgK0mSJEmSJEmSJElb4gdbSZIkSZIkSZIkSdqS3Am+n9HOzs6Vw8ezBzJnD4ym6+hQ6uzByNkDjynNdGg2PTeb5j4xOpA5e3h1n4PhswdpE8oXOvg6m39UbhSj36N8ofwj2YPX6brs+1L6svWPDvqmMqI6lG2XfQ6kpzLKHkifrbv0Htn8y/Yb2YPX6WDzbJrpfekZ0+k09Qzq17IHr1Pd/fDDD0Ps8vIyxPRsabpcXVt3sb5kf6+BexdNrGu7g70QG1exv6LYZLCTSt+zbtksQmxU58bVTftg9k6I7Q8PQ+ydi5+EGPU5w3oYYl/Z/VqI/W9/9a+G2L3L4xCrYfzouthfbdpb538UYm+extjXj74RYnd2Xt1oWv7MjV9JxT6cvRdir0M73xschNgfnPzrEPvJeawbv/He74bYzelRiF3OYh3/97/y8yH2xv6XQmxYjUJs08oi5ksBeVXAVLksYS4KsaxBGdtMXcH8voj1fgT9ZJ+0qL+dybTY3flkrFrDXG8Ec/S2za2FaL6Wvg7qUAV1jepf2cV6VTbx95omzgWyae4wzVEN99Y1tEta59F8HMaUusuuEeNzR8PcvQ2U+fn8LMQqyPvz5Tn8Iuwf1HHtNy5j/Xv18HpMH9aXXP9C5UF5tYa1Wguxi2XMlzPIq2kd10dHu9dC7NG9rI9BsTVQJ68NboTY4fAoxFZdXEfRWnJYx7Qczx6G2NnyNMROoB6MoMwXsKa7MY55RXUjuWWEdYP2I2h9SQ/hPa1463gSn7EzgfUCvAfuHyxzexS0z3B6EstoMY/zIT1dZVleWfdm92CGwzg/o30ZimX7Sqrn2f3mPnuetC9IKF/o3bJ5RX0CvQc9I7t/vem9R/o9uo7Qddky75M+6mfpuVRu2T3y9NwuGSP0bvRckn1uth0RakfZtk9t4datWyGW/aZCe7J078XFRYhRvaL95v39/RC7cSPOiajtZ+tutu1Tm3777bdD7O7duyE2m81CbBtcsUuSJEmSJEmSJEnSlvjBVpIkSZIkSZIkSZK2xA+2kiRJkiRJkiRJkrQlfrCVJEmSJEmSJEmSpC3JnST+WX90MLhyuHL20PHswdJ0gHL2XjocOhvLHjadPRiZDkHO/h4dXp09zH69Xj/2cykP6PeyB3hn85nSQoedUyx7KHX24PBsfSHZepC9l96X6gbFSPY9qP1myzJ7kDu1c7ou+26EDnKn3xuNRiGWPeB+sVik7qU8zebz+fl56hn0bvR7dMg6HT5/dnYWYvP5PMSy/bO2Z1Dl2hH1Ocs2ttWqgD62i3VtXMdxa1DFej+C66ryyf/NWdPG8a1O5lUfT+PdjpcPQuw3P/inIfZvj38UYosmlvmLuzdD7Nde+NUQ+/L+10OsLMsQ+0+++j8PsdPlcYi9fRHTV244/965+HGI/R/+8X8WYv/0d38QYv/jP/9LIfZ//NX/fYjtj44eL3GfweH4RohdrE5DrC1iWz1ZnITYexcfpJ57fXoYYpNJHFd/727M5//wS3GORGMK1aE+yiL3e9hflXFu0cd0sLPR39N2VXV1ZSylujuE+Vp2LrVY0To5tuk1jG8l/S13C+0N2ke7js+gNFfwvh3EaM7fwJy1pvURzuVju6S00DOaFtJSUfog1sDaCvJl0cT1wsN5HKfPFnHO38Kca2c0DbGb0zgG7Az3Qox6v9fHuyG2bmP/fLGK6cN1N+RLWcbrVrDPMG/iOuUHD38UYg9P49rlzo2jEDvajzHSJtsg7lFAPS0bqH8F1aEYu1xchtjpPK7L6PcqGLeoH5p38d7pTqxX0zHM0SEPaM25gvV+SevkQUwz5Qs0hWI5i88YQh5QuXGaY51cwPr38jKW0YL2MSnReqrW6/WVNkD7c7QvQ3sr2f2l7P4roXtp74z2oUifOTS1Q2pLtFe9txfHn+k09jHZ/XWS3Xsk2f0+ynuqL5TP9Hu0n0Z77n32FLOydSP7TSC7R5m9l+oa6fO9iJ5L+UKxPvV0dzfOu6h/wf05qJO0n5uNURlRW6V2nu0n6X2z33Ky5Uttn/bSaT98G/wXtpIkSZIkSZIkSZK0JX6wlSRJkiRJkiRJkqQt8YOtJEmSJEmSJEmSJG2JH2wlSZIkSZIkSZIkaUvi6b8bUFXVlcOBswctZ9GhynQwfPbga/o9OhiZDijuc7A0/R7FsgcoU1ro3uyh3tlD0el9qTzo3ej3sgebU5qz6aMyz9ar7CHX2ffI1pc+v5fNe7o3e9B3tsyz70vpy+Z9nzzNvi/1L5QH8/k8lRY6ZJ3SRwfI06Hoo9EoxOgQeCqjxWIRYicnJ6nrsv2VPj+oZ2o7GN+63Jhc1FP4Peivys3OLRpKcxGf27SxDY6q2N76GFTDx7533sT+4O7svRD7P/3G/zXE/vHv/SDE1tBH/NovfC3EfvkXfzHEvnLwjZ+azsexbGOf8+Ozt0Lsjf3XQ2y2vgyx6WAnxH5w8q9D7L/4wX8VY//FP4gJfOcihP7eOJbl//JbfxRiv3D9u/H3Nozq6Wh8M3Xvq3uvhdjxMo4Lu8OYp0fjwxD79Zfj7/3lV389xF6YvhRi2flQ1rqNfdMa2nkJfU5dxjn1pvsmfc6U5R//vz9Rwfqjj3EV28dqFevzusutceo69mG0ZmpLmrfH9GXbb9PCmriBMbnHtLNN7guUsDagWNfCOgXmEZerOFZ8//6bIXb/5Ax+L7p2ENcQN6bXQmxax/6ZSoPqJPVqS1gHzNZxDrIDYy3NuQbQn1425yH23vkHIfb++w9iAufxGfeG8RlnRzGfDzpYE1O+JOszrRGp/mXHj0kV13SLOpbHCH5uVMe5wPVpnHvfnF4PsSmM8QXtGcHckfJgAeta6ofGk5jmroT1+SqO5/SMIayTaSZP62TaAzg/j/UU95GwBWvb2ra9Uta0t0LlTvstdB3psy+d3X8lVKdpf4l+j55LeTAej0NsOIxzCXo3uo5i9FyS3ael38vueVLs4iKO85T31HdMoT+mvULKZ8pTnNdAHmS/J2S/s2T3c7PfQOh9Ka+ye6j0HpRXffbX6RmUPio3uo6eS7L73FTvqa7Rc6neUz5nvwlQ/mXfl8qNZL9ZPCvcUZAkSZIkSZIkSZKkLfGDrSRJkiRJkiRJkiRtiR9sJUmSJEmSJEmSJGlL/GArSZIkSZIkSZIkSVsSTzbegLIsrxxynD3Q+qf91p9GBzL3Qb9HMTowmg5GphjJHridRb9HhznTu2XTTOiAZzrMmdJHKM0UI9nDzqleZQ+qzr4H6fNckj2YO1s3su2S2gKVOelzADq9W7Z/oVg2/7Jtn66jGL0b5QHVjYuLi1RasvWU8oXunc/nqd/Ts6dp18W6/aQeDqrNjqHDKtZnqkOrNvYRI7h31S5DbG948Jipy1tD+soC5iDV8ImnJetyfR5iP7n4cYj94ckPQ+y3vv9miP3wzXfjQ9axf/kbf/67ITauxz8tmY9lCfXgt+7+Zoj95nu/G2I/Pv1JiF2+cBli18bXQ+zt83jv9+/fjQl8J/bFxc1JCGHfXj7+uE/m6/hu8zbXZx+NYh6QZbsIsRuTGyG2O9gNsS8ffDnE7s3uh9gLO7dDbFJPU+nrg8bBpotzgQbq5LiOZT54MssrfU6U5R//v49U1eP3BxXU3SHWvzgmUx9b07q7pnVyHAfb5Pqjza412jh/LhuY38M4nUX5V8M6uUyuk6nfWBax7zxbnYXYw/M4plxcxn68gvfdvRn78aqMY08L5VFCrOtg7Qxj2cnyOMQezk5C7LKexWeU8bmTQexPV12cm82obixgLTSCPKC9mwZiA8orqAcwnq/WsW3NVjEPCOUB7h8M4nN3ip0QayHNu8M4Tq87WK/S/L6B+X0T35fqWkPraeg3hsPYBrsyzgU6aPpU70mXbNNU7+ne5SrmgZ4fo9GoGA4/Gdeye7LZvaTxOK6RaHykvR9aR2T3ebJ71dTHPJofnxbb3Y39yc5O7IvoXnqP7Ltlyyi7xzaZxL43+4zsN4vsnjbtFU6nsQ+kNFP+LZfQR0M9oDRny432Cum52XzJtgUqI9qXns3iGEz7tNnnkuz+erbeU/oWizinpDQfHMS9s1u3boUYlRE99/w87jllvwlQmVPdpXpPsvv6x8fHIfbWW2+FGL3bs8J/YStJkiRJkiRJkiRJW+IHW0mSJEmSJEmSJEnaEj/YSpIkSZIkSZIkSdKW+MFWkiRJkiRJkiRJkrYknv67AWVZXjkImA4ZJnRQMB3cnP297OHQdJB2Fh1yTc/NHkCdPdicDpYmffKe0GHY2TQTygN6N7quz6HelObsAffZZ5DsAeN93o0O+qY8zdYhOkyc6ku2LLMHudN19G6Uvmx5EHq3bIyMx+MQo3ejOkl5SuiweGr7dJA75VWfPlHPnqqsiqp8un+fNapjvb8+vhli6za231EV6+nTMKhivV+3sc/pisfvX7JOl8chNoR8oesezB/AvbE/+PaXXwmxuo715KUXb4TYf/rN/1GI/eL1PxtiffzB8fdC7Lffj7F/+OaPYlpemofYr77w3RAbQL78yq1fC7Hbv3orxP78/+VrIfZHx++F2P/gjb8QYjuD3RC7WJ1B+mKdHNeTEHu4vB9id3ZeDbE+vnrwcyF2bfwBxGJetdDO/9yLvxpie4P9EJutL0Ns3cYxb390FGJZdRnH5N1hTMv56jTEmi43n8yO52TTv6ft+tNjcp1cr2brQQ3zv9hrFEUBVYieMRrGsYfaTBwti6JLrhdoHUBjLeYBvEh2D4DyiqzWi/hcmldB+uZNHI8q+Jv5/Z1YSvRuu7vTEHvl4E6IHUKfmF1Ltk0st7PFcYgdL2Kf+HAW33dvHH8vziwKnF0dDA9D7KtHcY5549uxzz5bxPHjGvTtBSzpWujbS0hgt4YyX89CbAzzYtwzonUylNvR8Hp87iq+b9nF3+u6+MJHUF/qIt5L8/a2hf2wMte2hrA2pT5x1EL+Qb5cljHvC9iqorZP619qH9n9NT0/6rq+Uq6TSeyPs/uHdF12nzs7dmX3pmjvLPseVM+pjVCM9pzo9/rs02b3AOm5tBfXZ3+T0HWUlmy+0PsuFnFuQu9BeUrp2/QeKuUf7Y1SHaJ76Rm0D0r5QvlH7a3PPiiVZZ99ZCqj6TTOAbPtjZ57cXERYpeXcS4xn8e5HcVmszgG0zMOD+PcLvseVJ9PT+N89Ac/+EGIPXz4MMSobjwr/Be2kiRJkiRJkiRJkrQlfrCVJEmSJEmSJEmSpC3xg60kSZIkSZIkSZIkbYkfbCVJkiRJkiRJkiRpS+IJvhtQVdWVA5Lp4ObsQct02HT2gPbsIeb0jOzh5CT7bnTgdvbdsge+0710MHf2EG7Kv+y92TzNHgJP12UPJ6c0Uyx7MDzlC+Vztp5mD7in38uWBz13tVqFGL1btv6RPgevZ8uS8o/Sl23TlAfZcqMYoXejZ2TzL1sPKF8mk8lPTaeeP92f/H/bNq13Q6ytY90ti1yb2bS6hDGlgjlI+eT/1u3u7L0QOxpfC7Hd4X6IffPoWyG2M9gLsePFSYj9uZcfhNivvfjdEPv5a78UYn3cnb0bYr9773sh9o/e+lGIDQax3P76l349xCj/Xt/7Sip9v3A95sG3r/2ZEDtdPgwxanvnq7MQ+827/zTEbu/cDrEK/tby7uxuiC3bOAd5eef1EBtUuaUAXUf1alSN4s0Qe2nn1RDbGx6E2AXk1ZvnPwyxF6d3QuxodCOmr45z7yx636ehLWAOXOTmL3r2lFVVlI/Mxwa4To7zzraB8ZLWycm5aAV1qO1yz62h36U5dfy1oiiTc9vxiNbJuecOIDaE9RvlFa2FLleXIUZ9ybCMZTnqYv+3M4rzodVBfO5qP+bg9elRiB0MY6yq41hRwvylhTXODN73wWUc3x7O5iFG6+QXdq+HGFWOYQVtAerLPowVB+PDEFu3MU8XsD4vyviMh4s4H6LxjfKvqWJ93oOhcVJOU79Hq2TK56KL9blraf4fr5sMYlrGw7gebNq4vjxdXMR7S2gfg5gJ2bU4rruhD6M50grS3MA6uYD+YDSKaR5CTM+34XB4ZU8kuw+a3auhfcHs3i2h36Oxi2K0R0RppnkDvS/J7lvScxeLRYhRf5fd66cYyeYVPTebp4TyKruPTNdly5K+RWT3GbN7/VTH6bps3aAyyu6hZvevs3v9lJbs/nC2nVMZTadxrM4+9+Qk7v3Qvm+fb2vzeZwXHh8fh9jubpwHExqDLy7inOOtt94Ksbfffjv1jGeZ/8JWkiRJkiRJkiRJkrbED7aSJEmSJEmSJEmStCV+sJUkSZIkSZIkSZKkLfGDrSRJkiRJkiRJkiRtSTxReQPW6/XPPEiZ/v9nD6/O3ps9KD17uDbF6IDx7IH0dC+h98geZk+HmGcPXs/mVbbcSPbA7WxasvdmY/QMerfsYeLZQ8yz9Y8O4d7Z2Uk9I1uvsu2NZOs9xegZdDB8Ni3ZA+npumz5bjpPqb7Q4fOUL9T2s31Ytm3p+dR2UMeLWHepPvdBv1cXsZ4+S6ryyf9d27JZhNi9+b0Qq6vYH3w4uxtiXz/6dry3jPn8N7/yn2aT+MQ1XRyjvnH09RD7j75+HmJ/7bW/FGLfPPqFzSTsU1DdOBrfSN17bXwTYvHe7x//fojdnX0YYjcm8d5JPQmx3/rwn4TYqIrziPvz+6lnXIfY/vAwxMje8CDETpYPQ+wnFz8OsXfO3w0x6sNuT19KpSWLyvxyHevkuJ6GGPV1TQvjbwHjNFw3HcS5np4Pq/WqWK1XH/9vGmnL5BwT1yTJe3GMj9NijLUwf8b1DMVoDQbzzvR6lebeEFutViFGc+91E9vbso1rbOoP1qt43e5wP8QGgzhvf3n4KjwjNw/jPIVYkdujoHniLvQ5t3bidS/s3gqxPcgDSnNdxzxtm9z6jerpdBTTPBrEdRQ9Y1LEfvxseRpiszbO4XYG8V6qLw/nD0KM3m22uIzpG8ZnTEcxNqlijJTw7zjmy1mIXa4v4nXNPP4gbAVRmqmellWuvxrAWrceQqyOiaF7O6j3q3Vs09RH6Pk2Ho+v7LH02cOifZTFIvYTffYoad83uy+d3Wek52b34miPMrsvTXmV3VumcT67J0Z9TLZ8Z7PYV1JaKP+uX78eYnt7eyFG6PeyqNwozZQv02nsyynNlPeUVxcXcVzJflOhukb7pdl5MD13Po9jHLVBilHdpXzJ7n2TbFqoPvfpX7L95Pl5XCvfvx/3GuheKssPP4x7IT/84Q9D7PPAf2ErSZIkSZIkSZIkSVviB1tJkiRJkiRJkiRJ2hI/2EqSJEmSJEmSJEnSlvjBVpIkSZIkSZIkSZK2JHd692dUluWVA5zp4GY6UJjQQcZ0OHT24PDsvYTupcOhs4dXEzqUus8h69k8oBjZ9HXZQ+8pRod1Z98te9A3xag8Nl0nqX1k85QOJ6d2RLL1mWSvI5Q+ajN0qDyVB/1e9hl0HeUL6VPv6blUT7Plmz0EPtuv6TnWdX/8//5EWcbybQvorzqoG3Dv58W6jWNoW8R2NKpiP9R2MG510L/A752uHobYqo1t9bfu/laIfePo66nn1mXsS/o4X52G2Puzd0Pstd0vhdioHofY7cmdEJvU0xB7de/VELuzE2PPo1W7DLF3L2KeDqo4BozqGFs0C/i990JsDOXxn/32/zvEvnXrVoj9ldd/JcTuTF8OsclgJ8R+dPaHIfYv7/3LEPtwdj/EjufnIbY/2g+xp6Eu4xxkCXk/qOJ1FfzdLMWo39DzqyrLonpk7oXrnobWyZudZ5PseoZGlIrmwLSugLTQSnwNeVDV8fdorkxtZrWOYzytIdZdbo/iZHkSYgeTgxDDtV/qCUXRUl5BeayaOH6si/i+08FuiNUwr5sOY589gHFmfxL73b1xjGG9oj2eEOE5a9NjnTwYxPeg8beDfF7CdTXUyWxaFsnn/tHxByG2B2vil45uh9h4MgkxmhNerC9C7GR+HGLzdRzfGmjBQ5irUFl2BcyVW1jXhkhRVLROhnraVrm9PtoXqGG7tKofvy/Ws2kymRSTR9oK1YVsu6b9lvl8HmLZvVsa4yhG+2R0HaG00FizuxvHkOvXr4fY0dFRiNH+Es1Nsnt7ffZpqXzpfRcL6O/g3tlslorRvRcXse+9cyeui7N7dtkyp/ddLuOYRCifs998KF+o/pE+e/3Zdkl5kE1ftv5l+5LLy8sQozpJdYhi9B6UV+Nx3BvY2Ynzwmw+U92g98h+I6Ryy35ve958fndeJUmSJEmSJEmSJOkZ5wdbSZIkSZIkSZIkSdoSP9hKkiRJkiRJkiRJ0pb4wVaSJEmSJEmSJEmStiSeirwBVVX9zMOu6cBjOmSYDhj/ac/80+ggbfo9ui57L8keCE7X0WHxdG/2UGW6N3vgO8keYp7NA8pnerfs+1L6KJY9zJ7SR7L5R9dlD0rPpo+uy6aPUPqybSYr2z6y9YreN9u/ZA+B79O/EHoPqqfZ9kHvS2mh5w6Hw9R1fcpcT0/3J//vI9iOOijf4otVvoMq1vu2S7bfEsZaiJGbkxdD7FdvH4XY5fo8xK6Nb6aesWn/5P1/GGI/PPlxiP3qC98NsT9768+FWF3Fvm5b77YtPzz9tyH2D9765yH2y3e+GWLfvRXz+YXpyyG2O9gLsfdn74bY4WQSYten8d6j8VGITQY7IUaOxtdDbFTHOfCD2Qk8Y5x6xtNQl7HuVmXsNwZl7F+y8426i+O5nl9VVRdV9ck8v4J60Kxprvf462RcvyXXC/SE7LqMZOf3ZQHr5GHsI0oYazuao0NzqytaI8a2Oh7GPqfpYh6MB7HvJPi+VYx1Lawr4Pfuz+6F2LxZhNj1nWsxNrkRYkPIg2ER875tc/UgvU6GGM5ZaypzyCta5yXXLhfrixC7NzsOsaNxHBvHVawvlH/XRrE85utZvBfWg5NRfAaNoSOou1QewzaWOVlDvad2hGti6DeojAj1k7zvA2MyzOWpj6U6VA9hnVzTeE77EV+sddTzrG3bK/say+UyXEPjHu3VjMexzWX3c9PjI8SozWX3binN9G67u7shdu1a7McODw9Tv9dHdh+K8oryPvt9IrsPmt3nprpGsfl8nvq97N7yBNZ62bkdpYVk91opLdlyo7zK7nlSnaTr6BsNoXzOfo+h983WP9q73dmJ63H6vaOjoxB75ZVXQozy6vj4OMSy9TS7B73p7w7PG/+FrSRJkiRJkiRJkiRtiR9sJUmSJEmSJEmSJGlL/GArSZIkSZIkSZIkSVviB1tJkiRJkiRJkiRJ2pLNnvz9J4bDIR4Y/Cg6KJgOQaYYoUOaswd902HYdHg1pYWeSwco0zMolkXPpTynw6HpXkLpy74boXvpYG76Pcr7bJ5mD16ne7OHz9MzsmnJHjqePXid0pd9Rlafw7+zB773SV9Wto6TbF+SrX8Uy5Y5xbJ1jZ5L5UH9C/WTffo1PRl1NSgG1Sdl2na5tloWT74NPksoX6pyO3/XNq4nITYoY7ucN7MQm9TTJ5KmR/3uh38QYv/qw3dC7HC8H2JfOfx6iB2Nrm8mYX9i1ca+6R+//9+G2Lgeh9j+MKZ5f3QQYq/vfeUxU1cU9+YfhNj/581/EGI/ePAgxF7ci/ceDK+lnntjcjvEHu0bPvIXXv1qiD2YncTnjmJepcFYMSjjeNRAu/z5m98MsRd3Xnz8tPRAfUQLw+Cqi/PJuoP+heb31SjEsvMNPXsGg7oYDj9pdzRtqpNr2BZipIPfozrUtDS3jb9HNW2Nc9F4XVXBGozSB8/IzkuoLQxo7k3rD/i9Gv7Gve7gPQqYRyT/Pp7yhaYgVG6ni/MQO1vNQ2w4iHP5g8lhvA76HCxzWCc3DZVlTPOD+b0QqyEPhjBGDeuYvulgJ8SofVA9XUP/fPcipu8c1j3jehFiNWyxNVDH6Tqaw93ei3OQVRvX+zSep2E7z7W3/dFeTEsR6xrlPalhvVrRehXubQbxutUK1j3wamUFa+c6t29B/ct6DXtV2LPpWUP7HhSjvRWK0fhNcFxO7v3QvT9rT/4j2b1H2vs5OzsLsZ2d2B+PRrHfprZE6N1o7zG710X3Zr8nZGXTR++2WMRxZT6PY3p2j5zyuc8+KD334uIixKieTiZxj2M6jeMelRvOgyGfqa5l91opX7J1KBvL7g/TvdnypbKkbxu7u7shdnx8nEpfn28M2e989G6Uls8r/4WtJEmSJEmSJEmSJG2JH2wlSZIkSZIkSZIkaUv8YCtJkiRJkiRJkiRJW+IHW0mSJEmSJEmSJEnaktwp35/RaDQqxuPxx/87e4g5oYORs4eOZw8JpwOP6bl0+DfJHoKcPXz+0bz8tGdkD5sm2QO3s/lM+Ufpyx6ATjFKc/Z9sweW03tkDwnPPpeeQbLtiJ6x6brR515CeU/tjepBNi3Z/iB7MHy2HmQPvad3o76JyjIrWx5Uh7L5rGfP5eqsOF+dfvy/qzLWv6qM5TsshyHWdLF91NUTmUo8dZQHzxLKZ+qvHi3rj+wNDzaalv/4q/9hiP1Pvxbzb3ewG2JHo+uP/dwW6t/v3v/tEHv/8oMQ+7t/+I9C7PXDGyF2MN4LsRvToxB7ZfeNEKuhbZGD0bUQuz49DLFh9U6I/Q/f+KshNh3spJ5LDqE8/tYv/q3H/r2so3HM+1+6+d0QOxxTvsS+6cXpS5tJ2GdE/UbTxbG7hr+Rpb6Yxmmq99nxXM+ealAV1eCT+lDS2FPCWqjIzRPhMl7TQYy0MCec09p5uUz9XpVcM9FzqdbTPJvmrDWthXq0I+rvmzamednA/kELawh43xreg0rtxZ3bIfYy5PN4GPcUqD/taL0P9WUF9eB8fRKvgz7xg4sHMX1VTDPl87COZfnS7ishll0nUx2ivBqsYh2/s/9CiE1H0xAj2X78a5Ovhxi2N3pf6iPAoIx5ejjKjb9URtNhzAOqz8Nh/L2yx5pzOID6DHV3ANdRv1FCr9O1sB9RQ5pzWa9nQNM0PJ4+os++VnYvM/t7lFa6LrsHSOmjvjK7X5Xd56a0YDtM5lV2f47eI7u/nr2X0jKfz0NsCX057Q/32TOmMqLnZvMqu5eebQuUFro3u889mUxCjN4N54pwHY5TPfZVs/vN2TylPNj0Pjxdl82/7Pcdyisqy729uFezsxP3Qi4vL0PsefNs705KkiRJkiRJkiRJ0ueYH2wlSZIkSZIkSZIkaUv8YCtJkiRJkiRJkiRJW+IHW0mSJEmSJEmSJEnakng68Qas1+srB1vTgcJ0kPFsNgsxOoCaDjem38seXk3pI/R7hA6lpkOQR6NR6t7sAdn0btn8o0OpKV+yB1/3OXSc0pI90JrQ4dUUI9m0UIzQYfGUB9lnZA9Fp+vo8Pls+sbjcYhRfc7WISqPPvncJ0+pzLOHtmd/j/KZ8oXKiNo0XZc94J7STOmj8qV+N9uf6umpy0ExKD99uK/o77eo/0v+nVfbQV0rH/9vxJou1ud1G+spqUvo/+DeRUv1Ob7H0fhGTF8b22BdPZEp1hW///D3QmxYxTHg5d3XQuxwdO2xn/vC9E6IXa7PQ+ygxzMoT//l/d8OsX/07m+E2OniIsRm0E8O65hX92cnIfb++b0Q+/nr/yrEvnXtOyGWNR3EcfXPvfx6iI3q2Bd/XtyCejUZxPkztd+94cETSdPPQn1dDf1tn/6PnkH6PENPT9e0Rdd8UqZUvjTXWy5ivV/DfI3mnctV/L1mnZsn0ryOZnoreAYZDGL7GI1jv0bTlq7Oze8pfTQv7uB9S1ovQJ62kC8ni4fxwZCYYRHfd5xcc9KKZDKYxutgmTyA+UHbwJodYh28yNkqjpfHyxhr4F7q1ShPV02sV3OIXU7iHORwfBRiA1gTlzWUOaRvr46VcrmAdVkF8w1YRxW0ToZYA/OXPutVQvdOh7B/NYxzFZorD6v4vhXlPTwX17DJdS32dVTHW3oGrF2gbqxWsTwGVDdKWKe4Tn4mrVarK/1tdq8wu0+b3Zeh8Zb2ZbJ7STTeUpqz+ze0B7i3t5d6bp991U3v2WF/N43jKL0H7X9RedCebLYeELoum6eEyjy7p9hnD59+bz6fhxh9G6J8nkwmqRilhcqX6jiVJV1Hz8juufdpH1QelM+0tqD6TGmm52b3/6nPobynOk737u/vh9j169dD7PLyMsSeN67sJUmSJEmSJEmSJGlL/GArSZIkSZIkSZIkSVviB1tJkiRJkiRJkiRJ2hI/2EqSJEmSJEmSJEnSlsSTfjfg9PT0ysHR2QOt6RBkitGBx3SgMB2gTIcv0wHP2QPfSfagajqkOXvAczbN2YPXCf1e9hnZ96VDpLMHqpPs4fPZd6MYyR7uTmmhA7ezB4xn8yV7WDzJtgUq82x5ZPOlT3lk6wHVvyzKZ/q97HsQeg96BpURXUftcjqdhtitW7dC7OLiIvUMbdeoHhejevLx/67Kx/9brXy/AfUAntvSdaBp49iIzwDzNs4juiK+R1VAf1rCGAXPXXWxvdVPZop1xS9c/26I9SnfrL3hQSrWR13F/KP+79okPpdiv3j7GyH2Z27+mRAbV3EOd39xL8S+chB/L6uGevXLL/xSiL119naIvbL7xmM/93m0PzzcdhI+1eX6PMSmg90NP4Xmp7k+Vs+ey4tZUVWf9AEljD3rJo55q1WMrWGuV8IceD6bhxjNExtcg4UQzzvXuXUyrReqAawDYAxt1nGe3dSwtq9grdHBHATSV8G7xScURQvzob3BPlwZ01Il184VrZNhrUHj5bDOjaHZ9RG9B103hLRMYE13NIl5NSkmITaoY76sYc61N4y/R/WAUJ4ejOI8ou5ieYyrmOY1/N6Q8pTKg/Za4D0qmEdk58UljBXpvaWG5sqpx2I+U6wPmqO3LfQb0JdQm6b+dDyOa+eja0ch9uh+6MfPffwtAD1Bw+HwSh9Me7I0ZtKeE+1hZdHYSrHs3l52b4quozGJ9oh2d3Nz3uy70XtQWyI4jib3aancKH20/0XfIqgOUfqyae6zp519brZeZWOUB5SndB2hPKBvPtRW6V76FpEtj729vRDL1iHKK/qGRPu02XpAeZqtGzs7O6nfy+Zf9jtatg+bTOK869q1ayH2k5/8JPXcZ5kre0mSJEmSJEmSJEnaEj/YSpIkSZIkSZIkSdKW+MFWkiRJkiRJkiRJkrbED7aSJEmSJEmSJEmStCWPfyL6p7h3796VA4jpkGY6jJgOLaZ7s4cR0+HG2QOt6fcoRoc+E0oLHSKdPQSentsnRiifs4d1U7n1OTydUF5l341kD0/vk890EDnFsmnJHmJOMSoPKss+6Bkk+x50Xbb+EWqX2faW7Zuyed8H/V720Hu6jg5ypz57Op2G2MXFReq5enrKsiqqcjN1LtvHVmVsl9ifFjAGFPG6QRXrX1HEGN1btTEty3YRYrPmMvXcB4sPQ2zRzOHe2LaGVRz3dwf7qee2Xeyb1m2c00wHOyH2NFBa7s7eDbH90WGMDWPs/vxuiE3r2OfcnN4Isa8ffi3EvnzwjRDLtosXd14JsVW7TN27hOsqqPeTOva7lL7z1UmI7Qx2U2nZtHkzC7EJlFGf3ytoTgj9UJ/nZp0sH4ZYDX0d9Wt9tF1uDC07mHtvqO/X5hwfn1yZf1Ywjx0k57YlzSdp/gyxNa11YZ1MqzKa1/G6LN67LuIz1iuYe9dQn6EtzFex3yir3Ly9hnF6NIjjdAl/4961Mc00BxnC73FZPv6anTSQvnUX1z2DOs43BmXMlyXMc6ivo/w7mh6F2O5gL8RWUP9qqAddC3W8gDrZwL4K9afQPqhvryAtRZnbtyAVlG9LdQPXl/E91k3MP6x/yXq1XMf5S7OGtgp1l+bAmFPJvRayamN9JlUFaxLY8ihpnUz98yi+G+1HjMbjEJvPYzuistTTNRqNivEj5ZUd45bL2EYoRnvQFMvuEWX3l2ivK7uvRXvVOztxjdlnL7PPvv4Y2he1Q9rDojzIPpfyhWLZd6P6QteR7L45/R7Vv2y97xPLfgci2W8l2b1HKl/qo8lsFuee9G7ZGJUlldHZ2VkqLfQeVNfoXnputj5n8z6bPmqr+/tx74za4OeBq3hJkiRJkiRJkiRJ2hI/2EqSJEmSJEmSJEnSlvjBVpIkSZIkSZIkSZK2xA+2kiRJkiRJkiRJkrQl8YTwDTg+Pr5yODAdCH5wcBBi0+k0xOgAdDqMuM8B7dnDkrOHetO9dHAzHdBOsewzsnlABzeT7OHfdF32oGpKS598zj6jz3Ozv0exbBlRncwePp9FaelThyj/srHsAfLZ8iX0DOpL6BlUx+m5dB09l+6l96V2RL9Hz6W+hN6Nyjz7btSPU5rpUPlse9PzqeliPahLqJNFrh5UZa5varvYPtoutq1FE+vkqI5zlcPR9dRzT5YPQux3PvztEPve/T8Isb/51b8RYjfGt1PpG1ajVPqehq6Ief/+7L0Q2x3uhdjDxb0Q2xnE6+7svBxir+1/OcRGkC/ZOkSWbRwr3r14K8TGUEbTwW6IUbndGN8Kseoo9rtH45s/NZ2Pg9rM33v7vw6xD2exjAZVbNMv794JsW9f/4UQG1cxrxbtIsSo3IblMMQmdVxDZM2bWYgN4BnUlxDqXxbwjGz/18Bzd6BedW38PaprfdqC+js/Pyua5pMxcjSKZbS7G8uXrqvqWJarVRx/k1Pl9Byd53A0548Ppj5n1cQ+dnewE2Lj4STEygLm1GVsM6eLkxC7hHb5ykEcZ6gdDQaxjyA1zLMbyNOC9ijK3HWE1hUX64sQ2xvFsXZdxDpUFXE8mtSxPA5HRyE2GsT8w3VysqKu2/hus1V8N+rrqjK+RwX/hoHKfH+0H2KjKuZBDc/FtoXrZJhLnb8bYvN1HC8p93aGsR0dTY/ic5uYltkijmVUbgPYUqxhfkDlS7W5hfkkZEuxWq/idbTWpXoAW27YX1Wwrihi/Suhne/sxnkJtcsV7Uck5wfajNPT02Kx+KRNrVaxbtG+Ee17YN97Efsnuo7G+eyeDsnu99F+PT2XrqMYPYPeLbvfTGkZDuMYnP297N4j2d+P40B2D/DROvaRySSOIbR31md/mOoL1V1C70Z5T8+gdpTdN+9TX/rsN1O+0Htk84+eS783m8X5KN17fn4eYtl6kH3f7B50th+icqN+g66j9kHfDalOfuUrXwmxDz74IMQo7zf97eVxuWKXJEmSJEmSJEmSpC3xg60kSZIkSZIkSZIkbYkfbCVJkiRJkiRJkiRpS/xgK0mSJEmSJEmSJElbEk/v3oDxeHzloPHd3d1wzd7eXojR4cF0kDEd+J49RJquI3TgMf1e9qDl7EHadFhyVvYw8exB3xTLHqhOZUS/RwfI0yH1pM979Cm37MHhhK6j983WNbqX8pTSTLHsofKUL4vFIsSo/mXfg9KSPRSd0DOyh9lTmum6bJqzh5hn6zPJtn3Kl+xzr127FmLZepBtM3o+lWWsV10R21FZ5Opz2+XG7gqeW5WxXY7qcYjtDw9TzyCHo+shNhlMQuzO7q3U71H6+vjD0z8Isa/sfyPE0v1LG9vv79z7zfjc4z8KsdPlaYi9uPNiiNVQblQPXtl9/aemc1PePn8zxL5//P0Q2xvG+e6tSSzzN/a/GmK7w/0QuzG5nU1i8HBxL8TePPthiP3dP/r7IfYb77wdYv+/f/gvQuwv/oXvhNid/fge//5X7obYizsvhNigjPOXRRPHlG9f//kQ2y+OQiyL6nNbxnEa+zWok9SKqP9rujg2Lps4f64rWJO08d62jGmhPpGWf3ydnoThaFSMxp/08dNJHCtoTTyB62h+ulrl5t5Nk7uOlDB37HCNTb+XG6en49ifDmCeTYZFXLtcNhfx99rcen9EYyOEaE5NeXp8+SDE9mAMoHUF5j30Q6frkxCbrech1hTxfccwB6H5Gq0lh11cW61gHVDB7+HaBd73bBHf7XwVy3dYx75uZ7QTYuMytjdK3+4g7l9VVEZQXy4WMX1n8zgfev8ijpenqzguPHx4HmIH+/E9xrAvcHvnLMQoXwaQf/TPPYZVvI72D7J7Hs06jvtdkRvf2o7GbqprsBanZg7ttytimVcV1Je92IetlnG+QX1xdq9Am3F2dlbM55/0j9n1UHbfklD/SX1gdp+bYrQvmN3jpXvH4zg20H5adi7RZ18Qx0d4t2xeZffO+qQlu/dNZU57bH2+T9AzKH0Uy+6X0jOyZZ6tV9l9bopl9/qz+6XZ9yXZ/qBPmWfzj2Trc7bN4Fwbypy+G+7sxPkK+dKXvhRi+7BPcfdunHc9fPgwxGazWeq5m+TqXJIkSZIkSZIkSZK2xA+2kiRJkiRJkiRJkrQlfrCVJEmSJEmSJEmSpC3xg60kSZIkSZIkSZIkbUk8JXgD9vb2rhxATIcR04HHdJA2yR4ETehw46zsYc6UlslkEmJ0qDJdR4c0Ux7M5/MQyx5K3efQdjp8ebVahRi973A4DLHsweZ9DpWnPMgeAk/P7XPoOD0je/h8Vp+6S2WUPQA9m/fZQ+CzB9wTejf6PYrR+2bLN/tu2XpPeUrpu7i4SF1Hh7ZTWrIxOsid+nbqI/RkdF1btN0n9bAsoL8qYj2ty1x7azto+/B7gzLWF2oLhNKcRv0zpHnTvnrwtRCb1HGM3xvENrNp33/4/RA7XjwMsWEV+/vv3PjlEBvAdb92+78XYi/tvBxi00Hsc967fCfEbu7cCrES/s5wWMW5xbqNfR3dW5W52MnyOMRuTK6HGL3vzckLIUZ5sGk1tLc/ePiDEPvR8YMQm8+XIfadb38lxK7D+PHKwUGIHY0O43W7r4bYxfo8xF4evRJil+vLEDtfnYbYuov1YAT1ZdnGMWpvEN9j2cXrhvU4xBroX8b1FK6L6ZvUMU9XbSyPJcZi+roi9tnjCtYk8B56MnZ2psXu7ifljOs8GGuXq1jmULxF28L4lhxr02sNnHtn5+MxLbRGpDnmGK6rKP9grrzX7sXEwOtS31nSWi259qM58MOLOP4uJ7F8J+PYVq9Pb4RYDem7PY5jz8Uy9rFUbLNVXNvvTWL+lTBeDirY94GHUG15dK76kSH8XhGLvJgWMa9ozjUZxL646JJzUai72X2aro1vfLqI49bFMtaX5SqOFXu78G7QPnZgHT8exv5+OoxjDxQv3ktrutUa9vWgzVC+UD2gelXEVyu6Ll63hvxbwDxn3cTrxuP4vsNBzGf6ZzBdGd9jPI2/t1zGtPTZs9Rnt1wur7Rb2pPts1dIbSS7p0Nr7+zv0T4exSjNNC5TjNKX3QPM7vdl932zaaH8y+4PZ/cFs3sc2Xzpsz+c/VaS3Zcm2ffoE6NnZNsMoeuy+/XZ38tel62T2b35bCz7bY3aAsm2BXrGdBrnZ7RXTf1z9jvGAexT0DhPeUXfvZ40/4WtJEmSJEmSJEmSJG2JH2wlSZIkSZIkSZIkaUv8YCtJkiRJkiRJkiRJW+IHW0mSJEmSJEmSJEnaktzJwZ/RzZs3rxxIfnZ2Fq7JHpROh473OdA6exB09qBlSh8dgkzX0ftSjNKSfY9sHmQPh970YfHZg777vAfpc6B6Nq8oRgdf98kX0udQdKp/y+Uy9XvZekD5ly3LR/uVzyqbp5QH2TqZlT14PXsd9aeUfxSj96VD1rMHue/v74fYxcVFiNG4oCelLMrik7rUFbE+12Vsq/hL1NfB335Vue6ql6aN9ZTejVrqwfDaE0jRVT88/cMQ++d3fy/Efnz2Voj9lZf/WogdjI5Sz121sc9+7+JuiP0/vvf3Q+zf+/LPh9hre2+E2LXxzVRaXt37UojN1pchtj88CLEPZzHN18bXQ+xiHfuS4+XDEDtZHqd+r+1iP7k/jP3aEvL5aBTr1XSwE2JPw+5gL8R+5fafDbHDUcz7l3bvhNh7l++H2Mu7L6XS8vr+l0Nsp94NsQr6ofPVSYi1RSyj4+WDEFu1cWysythfTeo4b6dnjOA66jtx3h4iRTEsYE4IPdakjnVoDfVv0MZ1Cv1e0WP+ov4ODw+vzJUuL2OfSPMwmotm14hUD0poCzSGErq3hlhZxxjN5UeDcYi1MD/FNViVq8+XMPYcL2P/suxi27pd3g6xIaS5ozUO9CXLLvZNHxy/G2J3DuMYtTeKffuohrRAPRjX0xBbNzEt42H8PepPh1Xsw+i6Nbzvuot1fFDGPozSR9fRP0MYQPoGVby3pTU2rU2bWJbrIqaP0FhxfRrLd1jHNI8rKA/I0/1pnKuQKYwpNDZSO2/b2C4pn6ke0HDUQJsZVDGv6gHEitw+Q7uG/SEo8rKDfpJiLax7oK8bD2HOsBvr33IR82o+n8cE6omp6/rKvtV4HNtcdo8tu2dCezCz2SzE+uyN4jgA77a3F8eV6TSOF4TqanZfK7ufRu2a8qrPHjSlObuXmd1bpu8JVA8IpaWPPt9PKM2UL5Tm7HvQPHixWKTSkt03z9YDimX3S+l9s3P8PmVEz6C86vPNgmL07YDSkv1uQ20m2z9TjNYg167F/Zu7d+M+1Db4L2wlSZIkSZIkSZIkaUv8YCtJkiRJkiRJkiRJW+IHW0mSJEmSJEmSJEnaEj/YSpIkSZIkSZIkSdKWxBN8N+DOnTtXDil/++23wzUnJychRgcPZw8Yp8POswd4EzqgmA43zsayB5ZnD4LOoveg59JB1ZTm7KHPdLB0n0PM6d7se/Q54J7Qc+n3sgdfZ+tB9mDuLHrGarUKMaoHdFg31Y1smybZ/OuDyi1bd/s8I1uHss/NHqieRXlA5Usxkr1OT0pXdMUnfWNVbrYdZX+vLB6/HVHf/ug7faSF2Kh6/LbQx7ev/UKIPVwch9i6jf3uw+X9EDsYHaWeO4T3/fU7vxJiTRfHgGUT237bbXbsmQ52QuwQ3u3m5HaILZo416vL2L/M1pch9sL0xRA7GF0Lsd3BXog9j+oq5svXDr8VYl/a/1qInSwfhti3r30nxEZ1nP+RZbNI3UvlRu9RQN2ldk59Dl1XlTDfLYchdrqO+TKq4ntQPd2HOt60sb2N6gmkL/axyzbmKT2X+slBFd9NT8/NGzeLw8PDj//33bt3wzXn5+chRvN2ii0XyxBbLWOs7bNOhvnpcBTrFcXGQ1gnwzw2u0YsknPlveF+iC2gb6I5+nwd21YFY8+Q1slFfN/be7fivcPjEMuWeVvCeovmTfBugzqWUddCv1HDOg/mB3TvGvq6MYwBgxr6Z/j3BfS+TQF1IyYlDfOZ9ktaGD9g7TwcxHw+qI9CbHcY5yA0TxwOYv5R/SNUbjTO0PjRrGGvBcbakn4PmuoQ2368jm5ui1ivsN+AujEYQr2H98X9K9y7CaGiqmF/CPKqqh5/faTNWK1WV+oOtf/sfgbtoxwfH4cY7WkvYaymvTjaf6X9IKq/k0mcZ1KMnkv5QntOdG+ffWnKF7quz34VoXejPKVnUB5k5zXZ/dLsXuEQ+rs+e/NZ9B6UL9l90OxecPZ7Qvb3KF+y70Z1g8qDYtSXZOeF2XKjNNNz6X3pOvqeQGmhPF0s4pyc+knKq+x7kGyat8F/YStJkiRJkiRJkiRJW+IHW0mSJEmSJEmSJEnaEj/YSpIkSZIkSZIkSdKW+MFWkiRJkiRJkiRJkrbk8U/g/hSvvvpqsb+///H/pkPC6dDiy8vLELu4uAix09PTEJvP5yGWPSg4e8g1HWRMB5HToe10HR1yTYcvU4xk3yN7+DKljw7NpnfLlkefg837HBxO9S9b5vQM+r3s+2YPiyd0L6Ulm8998j570DflX/bweZItS3pun/aRlT2knmTLMts3Zetk9rnZPkfbVZZVUZVP9++zNv08qleDcrjRZ2zab3/4myH237wVYwPom5outsE701dCbFSPU2n5+uG3QuxHpz8OsbfP3g2xdy7fDrFBFfP+cHQtlRYyrGJ/te5iXzwd7IZYA9fdnr4QYruDA/i9nWwSn7gWyvxptFsqyxuT2xt9BtXTdbsKMaoHLc1LYJg5SNa/QUlLH+hfqnjd/vAwxFbwHnUJ8yG4bgTvmy3zHWgLXUHjeWwfnAd6Wl548cXi+vXrH/9vmnfSPGyxWITYDNZbF7Ce5rV4cs4P7YPXAbBGHMQ6PoT5Ka0vcY1Dc+DkOvl0dRJi92cxRn1OA+VB4weunYexjz0qY381b2NZztaxzC+WcW+kbWMejOqYz2VynQxdCdbJAZR5U8T8G3YxD8ou9pMFLI9aCiZVyTpU0DoZ2kcH+UyGw5gvVQXvS+vkQcyrcR33lvLr5PgeNYxv/HOwTi5gnQz38jNo34L2vmAdSmvT+FgqymIA6+RmEGNdG9NMv0d1aAjvW5RU12CP4imv0RSt1+srex20L53dc5rNZiFG+9fZ38uiPWgaW6fTaYjt7e2F2MFBXDfRddk9bZK9Lrv/2mc/F/caIP9Wqzifp73v7LvR/lyfvUzS57tDNi3ZfeTsPiNdl80DKnOKUZppTk7XZb/vZPOP6hqlhebzQ5hnZuspXUcxkk0z/R7lafabSrauZcuD8o/qyzY4O5AkSZIkSZIkSZKkLfGDrSRJkiRJkiRJkiRtiR9sJUmSJEmSJEmSJGlL/GArSZIkSZIkSZIkSVsSTwnegNdee604Ojr6+H/Twet0oPAHH3wQYtkD35+G7AHPdJB79iBjerc+B27TAd7ZQ9uzsey7ZQ+Mzh7CTTHKAzrkmspyPB6HWPYwcTpcm2JUHn0Ouc4eZk/X0btlD72nekDX9YkRynuKZQ+u75N/2cPOs+0y286z75s9yD37e9SO6Lpsnkqfd/uj/RA7XSxC7AHMkb527TjEmi7X55BhFfv7v/zSXwmxdy/fDrGXd18PsZ3B7mOnhewNDzb6e/vDw43+3tNQlV+sv6EcVHHuw9fFdpS1buO4VUI+03UDWCKt2jh3bDuYWxRxHKxLmPPDdX3sDvZS11Ga9fS88MILxa1btz7+3/P5PFxDc6n79++HGN+7nfJdr2M7WsNaLY5GvBaqaC1E82da68Lce1THJ69grryE9dtuE9+taeO9VG41zLPrOubLjfHNELuszkOMxt/RMK5h6bm0JlnTegZClObhIObpAOof9fcN5DOtXbBuwHsMkmtnQj3xaJhcJ9M6qoX1UZnr78uqxzoZrqug3HAdimu6+L40liHohloI0rs1kJaS0gclR1mV3cug8h0MqHzjvWtYJ9N+TtnRXC9XvnpyBoPBlb1F2vfI7q3Q/vUC1n+E6irVo+weEaF3y+7xUoz2PLN7vNkYPXfTe1N0XXaPl9JCaP86u29O+nwTyO7JYj+WfEZ2z5Ni2byne7PfCfp8d6A6ma1DVL6Uz9lvEX323LNjYbZ/wTE9+R0omxb6lpNtWxSjsqTf24Yv1u6QJEmSJEmSJEmSJD1D/GArSZIkSZIkSZIkSVviB1tJkiRJkiRJkiRJ2hI/2EqSJEmSJEmSJEnSljyRk3S/853vFLdv3/74f0+n03ANHaBMBxTTAd50UPDZ2VmIXVxchBgdUEwHKNMzCKWPnpE9QJlkD/Cm36P0ZQ+qpt+bzWapeyl92edmD33OHmJOz6X6R2VOv5ctj+zB5tnDv/vUA8q/7MHhfWSfQe/bB+UBHfieLaNseRCqV9lnZGPU5ywWixCjNGfbGz13Pp+HGMn2ddJnke13t+V4cRJirx9eD7EPL38SYu+c3Q2xf/LBPwyx//6dfyfERlUc38jucD/Ebk1eCLG6zM2HpJ9m1cYxapisp31UUHe7Lo5lgzK3HKI047gK19EzynI7fzdbbem5+mNf++rXipdeuvPx/x6Px+GaEczNChjf1k2c29Ic83J2GWKzy7imw7ky/B7O72FaTL/XQIzmmGVy7ljRXBnSN1/HOesQ8nQObXrRxD7s/uxeiJXlbYjBvIT6jTqW+bTYCTFaww5pndzm1o0VpC+7bmyz83uar9H+C9xKszpcEyefS2uSGt6Xyi27P5RVJX+vbZP5XCX3r9axPhe0FKd1Mu1b0JoYyyg3b6+xf8mtu6HaF806vtx6ldsXoLRQPaA6uYR9ONJteB9En93u7u6VPZHsPjLtt9B+aXYvJLt3Rn00oefSe9B12b1W2m/O7pPR71H7ohilOZs+mpsQKo8+MSq3PnsX2efS+2b3pbN7/dn90j75R8+gd6PnUh2i+dRkMkndm/1+ki3fPt+kSHYOk/3GkG3T2bqRbb/Z5/apu5T3tDajupHdD39crtglSZIkSZIkSZIkaUv8YCtJkiRJkiRJkiRJW+IHW0mSJEmSJEmSJEnaEj/YSpIkSZIkSZIkSdKW5E4r/4xu3bpVvPjiix//bzrslw54pkO46TDiDz/8MMSyByPTM7KHu9OB5XQwcp9DlbOHk2cPcybZQ70JPYMOrqc0U5lny43ynmQPm6b0ESq3PgeCZ+tGn8O1s4edZ9sC/V6fA9Wzh4lnDwnPtq3swesk2z763EtpXq/XqeuofVC+kGy+UCyb91TXKEbvK/00XQH9ZK7rTPdXWes2tsGb0xsh9nM3vhJiN6ZHIbY33AmxX7v96yE2quK42sfROKZZXL6DariFlDz71m3sx4cbrqdZy3YRYtn51bTKzROrEubtUDfajuY+MG+H38vCZ0CniOuj5Puqv2vXjoobNx7pa2E8Gg5jeWTnYccPH4YYrrdaWG/BPKxKjperdewnO1r3ZNfJkGZKSwX5UkFbGJaxXe4Pd0NsOpyE2GgQ772xczPESuheaI5ewxx4OIzPwD2A+IiiWefm/FTXKEbpI+kyorUQ7aHQOg/XjbTOe/z1VgmxGtKM627af0nuyRBsq3AdtUpq001L+wzxXiy3ZNvH6yivOkgL/SD9HiR63cT+ivKgax5/36ykfbP0Ojm3jq9q2Fuq4PegLLUZN2/eLCaTT/r+e/fuhWuOj49DbD6fh1h2jze7D5qV3SfLPqPPnhO9b3bfPLuPl90Pz+4v0TNoXJ5Op6nrsvub2ffN7tll855k8y9br7J5Su+R3a/Pli89g75PjMfjEKP3yH6fyL4vybbpbD4Teg/q1xaLuKbO1oM+e/hUn6ncKEay9WV3N64PDg8PQ4zyapP8F7aSJEmSJEmSJEmStCV+sJUkSZIkSZIkSZKkLfGDrSRJkiRJkiRJkiRtiR9sJUmSJEmSJEmSJGlLcic5f0br9frK4cXXrl0L13zjG98IMTosmQ4jpgOU6fBgitGhxYTuzR4Svum0ZA/wpt+jw7rpuuzB13Rv9vcozZQv2QO8sweRU/4RqmubfrdsvSKUlmw9yKI8yB7MTbKH3mfreLaPyJYl9SXZewm9Rzb/sgevZ6+jvKK6S3kwHo9DjA5yp+dS+6V7qf32qWv64uk66EvoOogNys1Of2bNZYh97fDnQmx3sBtiv9d9L8RuT2+HWFnkxoo+zlenIbY3PAixRTMPsXE9eSJpetrev/xJiN2dfxBiqyb2dV86+EqIXR/f2kzCnqJ1G/viQRXbTAttsO3iGLWCsWJYxXFh00ZVHMsofdm2RWmmPCBVCfOh1J1s3cb6d74+i8+A9x3X0xCryh2I+Xe9T8K6aYrVI/Odg/39cM1rr70eYlRPaU74E5jr9ZkrkzXcm10nN03sXxq4DlOSTF8D9X5vFPO5gjw9X1+E2HQIbYbWxAWsEek6WuNUMUZz6raJsxps58PY/6Ee62R8N1onw7vRezSwDli0cb6xbJYxgWX8vd3RXkxfj/kf1VNsW3DvENY91GZoLdm08RfrCvZuYCije9smvgf9Xj2IMbo3vZdB4yDkKaHnFh31Odl1Muzx0F4BlNsI1s6DYbxu1UCbXse6Ww2oHcUYlaU241vf+lax/8hY/Oabb4ZrfvzjH4dYdr+P9mCWy1gXsmP1YrEIsew+WXb/FccfaEvZ/ebsnmx2X4t+j+7N7j1m99L77P+T7P56dt83G6P6R2VOdYhQXtGeIsWydXdnJ65Vst8xqMwpdnER54DzeZyHUBukfdXpNM4fs3lA5fE09v+ze9B99qUJlUd2/5ryIPvthX5vdzfu2e3txTnlBx/EPaJNciUuSZIkSZIkSZIkSVviB1tJkiRJkiRJkiRJ2hI/2EqSJEmSJEmSJEnSlvjBVpIkSZIkSZIkSZK2JJ4MvQF379698r8PDw/DNdeuXQuxb37zmyGWPXiYDmSmg7Tp4GG6LnsQOckeOk6HZmcPZKbr6KDv7AHtFKN7s4c+03V9DjbPHiydPQQ+mwf0DLqO0kzvRmnpU26kz+9lDxPP5ku2XmVly4jqAd2bvS6b91S+VA8I5Uu2DVL6qM1QbDKZhNh4PE7dS/lHqE5m+1N9vmXbVhqNjWVsM5u2P4zzHPL63ldC7KsHPxdiffrJPiZ1nJeQYTV6wil5Ou7P74bYTy7eDrHZeh5igyrWq0EZ+8nnU65dVmXsx0d1HD+2hdJHsU0/g7QdzE+7OGco2pj36y7OIy7XFyH27uVPQmwH2vSNya0Qm9TTmBY9EQ8fPrwy99rb2wvXHOzvh9jrr78eYh20VZonrmEuT3PWs7OzEKN5LM7HqS3AUEZzR177wXqGfhCM6jhGtRWkGf52fX9M43l8bkXzdpjbDqA8apgX45p4DfNsmvMP4tiTXfutoB4g2pNJzuWbBsocnjtbzUJs0cXxl/pTXEdB+Q4h72nuSKMg1dM2uU5usc1AfU6uBxEkelBBXWtj3lP6OhiPKF+yM9ZsPUDJPKhr6jdiGQ0GsV1W1FaHFIN9EOhfVi3sMcJ1ZQX9i+vkp+pb3/pWcf369Y//98HBQbgmuxdC/Sztg1IZZ/cPZ7PYV9J19Aza56G+LbsPn80DekafvdHsfng2Ldl+Nru3TKgOUZqz+32U9xTL5hXVDboXx0J4bvb3+uyh0nMXi0WIZcso236pjChGbTDbv9PvUZ6SbPulvL+8vAwx6nMo7+nd6LnTaVx30vcx+r3s9wmKjUZxfUDPpfZBefDOO+8URfHH+U31ri9nApIkSZIkSZIkSZK0JX6wlSRJkiRJkiRJkqQt8YOtJEmSJEmSJEmSJG2JH2wlSZIkSZIkSZIkaUviSbob8ODBg595aDcdPEyHu//cz/1ciGUPHp7P5yF2cXGRilH6swfNE0rz3t5eiNF7kOxz6bBpSkv2kPU+h53TweF0XfYgaPo9OuiZDtKmPKDypXyhQ8cpRs/NHjRPsvlM+Ueyh3XTweb0HlQ3soe7E8pTyqvJZJKK0ftS+VLfRPlMv0fvS2mm52ZjhMqDUFrofendqD4TKrdsv6YvHqrjffqNssi1mW2ZNXG+cW/+QYi9tPta6vfaLvZNVfn4f4vXdLGdD2Cq+KOzPwyxa+PrqWccLx+G2Hw9C7E/OP5+iF0fXwux3eFuiM3Wcf63B9edrc5C7P78QYh98+gbIdYWMe8HVa4vfpbM15chVkIdGhTP9rttui1sGqWPequO5gJdfI+dQVxD3JrcDrFJPYVYnFNX/g3vU3N6elpMp7FcHlXBmnh3N/Zhb7z+RojROEjzsOUyrplmsHamGNXdehDnk9nxvIJ6P53GeppeJyefS2MetdUpjB8ouRZawxqW1rUdpGU4jOu8dgBrkibeu4A5+mIVy7crYprnRbxu3MQ1ItxaLFbxuUULF1I3FKtVsTeiPRRYp9AjYK1LaO1HayaKDSBGcF+lgzoODW4F6zJKC63jaa+gaWmdHPOKcq+Fdpndq6qT+yDURxTJdXIBz6A+oq7j29U13At1t11D/nUxfe0aKiVm1bO9nvm8uXnzZnHr1q2P/zeN0dRusn3C/fv3Q4z2qvvsM1J/gv0TtIc+sex+c7pPgDTTM2gPgcqIrqP8ozE4O+fI7tll93OzMXoPuo7SR2WZLTd6bp/9vj57P9n0ZfdpaV+VxlHKq2xbIJv+RkPlQe+2v78fYtQWsv0Q3Zttq9TvUhll9/UJpY/6nKzscx+Xq3NJkiRJkiRJkiRJ2hI/2EqSJEmSJEmSJEnSlvjBVpIkSZIkSZIkSZK2xA+2kiRJkiRJkiRJkrQl8QTfDVgul1cOJX7w4EG4hg5Zp8Oc6ZDhF154IcS++c1vhtj5+XmInZ2dhdjl5WUqfXS4MR0OTWmmw4jpXorRc+lwbbqODlXOppkOr6aDpeleOjCaDpbOHk6+WCxSaelz+Hyfg9yzh5hn8zl70Dy9RzZf+jyD6lX23mxasoe2Z9sb/R7Vq+y9feoBHfhOsWyaqa+jfo362D7lQTE6kL7PwfDanraL9aAqn/zfeWXrRpn8mzOqp0+j/q3b2Bf/i3v/PMTuzj4MsW9f/1aIfe/+vwqxw9FhiP25F349xK6Nb/7UdD5qXE9S13354OshRvXlew9+J8R+9973Us/4+tFXQ+yVvVdDbFzFfu0PT/9tiO0MdkJs3sS53i/f+uUQ2xvuh9igin32zmA3xJ51E8iXZ13TwdwR2nnxFPor6l8um4sQq8s6xAYlzA+gXhVVnMeSEbQFeq7j73atVqsr8+jT09NwzRLmesNRrBuDQYxdv349xN54440Qo7Xu5QWsiWezmL4VzPVWMGeooK5hU82tiSmWXRO3bYzdO78XYqsirnGOJkchdrqM5VZ2MX03pzdCbG8ax5QRrAOaNrZfMmzp3vi+s3Us3/MmriFK6DunVZwfDAvom6B8l12sL6NB7K+aMqZ5ZxjHqKqA/hT6zrKDdTLt59BaCPp23AuitXiIFEVH62Soz0UB61C4l8a8bJuhNEOxFStY0xW4Ts7tKdBD6uR+SV3n2gKtQ89nsY4v5rGPHY9onRzzr+xi+qi+UH/QraEeQD11nH66Dg4OisPDT9ZUN2/GddPe3l6IUV3N7m/ev38/xPrsL1Gdof2l7P5hn/0+kt0X7PN71N9R30F5Su+b3f/q8x599qCprlH6KA+yv0cxSl82XygtFKN7s++b3c+lGKH0ZeejffryPnvQ2TpE70F9Ha1VLi7iepdQf7WzE+d2FMv2Ydl6kM0/6g8oTz+qk9m69Fn5L2wlSZIkSZIkSZIkaUv8YCtJkiRJkiRJkiRJW+IHW0mSJEmSJEmSJEnaEj/YSpIkSZIkSZIkSdKWxNN/N2C9Xl85pJcOh57NZiE2mUxCbHd3N8ToYGQ6GP7VV18NsQ8++CDEzs7OQuzu3bshtlwuQ4wOLaZDqeleygM6VJkOMKY8pbRkD6Qn2YO5swc8U7llD4zOyj5j04eYZw8ip1j2kPXsAdl9ZMu3z3sQejdCZbRYLEKM0kf3UiwrWw+oDWbrKR1sTv3GyclJiFG5UR+bTQvlVbYeUB7Qddqepo11bd3F2LiOdYhk+85Nq8pn5+/QZuvzEHv34v0Q+3/+q38cYv/el+Ic5P/8d/6rENvbmYbYK6/+l/H3vv7NEPvf/dLfCrFJHX8vq+1gjCpiPThdXIRY08X+ZVDFfmPVrkJsb7AfYl87/HqIDatRiN2YxLnj4eh6iFVl7K9qiH1eUHm8c/HjENsbxry/Pr610bSsoW+aNbEOjavYN9VPZplzBeXVsIxzeaovNdTxPqjN6NnTNk3RPDK/m8EceD6HNeIo9mG7050Qq2B+dXh0FGK3X7gdYvcfPAixy8vLEHv48GGILVexf4ZhoWiq2GZWsE5ezOchRvPJltYukKfrJqZvVcTYu+fH8Tpo5//23XdDjGY5u7tx7+G1W7Gf/MrRl0NsAP1Gdu1H4y/NvZtVzKsS7sW1KfV/MNbuj+NYUVewbqziM8aD2LfjfBKyherGptEzaHaA657kvHjdwl5aE9tlu4a1Gu3JQFra5DqZ6hWNg1S+g5rWg7BfQmmmdTKkb76I/cblecwrUk1gnE6WWwdpKZPrZIzBvXpyJpNJMZ1+sgY6ODgI1zz6//8jKxj3aHycw3hGMRpvqb+jPWPalyG070ZpubiIc+3Dw8MQw3Flw3ts2T2E7B4g7bFl97qye2d9ZMd5yvvsfi7VXcoXkt3rz6Yl+4zs79Fckd6NYtk9LGqDhNob5X12/z/7HahP3tO7jWANki1zKg/al6bnZr8xZPels30E5emnleWmv8t8xJmAJEmSJEmSJEmSJG2JH2wlSZIkSZIkSZIkaUv8YCtJkiRJkiRJkiRJW+IHW0mSJEmSJEmSJEnaknj67waUZXnlIODsYcnZA57H43GI0SHwL7/8coidnp6GWPYQ+AcPHoQYHRidPbSYDrnuc1h89jDs7AHUlBY6MJpQvmTfrc/h7tl6lU0LeRqHz2cP0qb3pVj2sG66jlA96HNQOpUR3btcLkOM2lH2fQmlL3uIefagdEJtJvu+lL7sYfF0HeVVth1l+6FsX6L+uq67Un5tF+v4uov1imJ1G+vpoMrVoW1pu9iXVOWT/3u1BvL5H779r0Ls/OwyxP7uH3wvxI5/4ycxBs8d/ruxz3n/5ZMQm9Rx3tTH/cUHIXayiM8dVDF9r+7dCbGvHnw9xI7GNx4zdWy+jnk/rGI/+Xl2soxz27NVnCv/8w//eYjdm8V7j8YHIfaXXvpLITaq4lx+1cYxbwV9zu5wP/5eHX9v06hNt0XsXwqaX1WOefpj3Z/8v4//dwv1CsYtqn8VrZNHsS1MYO1869btELs4vwixxWIRYkuYs65PYn/fwJy1gfelOWZ2Pk5wrVvE2Ol6FmJtG6+7e3EW03cS+ytau9B892IR9xm6Jpdmml9RbAn96WIVy5LedzqM4+DuYDfEathKKmmdTGmG61qoGyXM1+j3KK+KZL0qkmu19Do5eR3V08U61o3FOpbbg9nDEFtexDIfD6Dt79wMMRqjqDzWTWzTtA6g59aDmC81zAnJGsqN1skNpI/axwj6SWqr2bKkOoT7ORCj51Je6clZrVZX9mJo3KPx5+joKMRee+21ELu8jOuNExgzz87iWHN+fh5i2TGTZO+lPac+z8juDdA4mh37s+Njdj+S2mZ2T5beg2T3fbP7bvRcqs90XTYPaCzM7kGTbH2htGTv7VN36bn0TYqeQd+VaN+XZPOZZL/l0DMozX3qZJ8+J7vPTe9Be9/Z9jubxfVB9jvaJvkvbCVJkiRJkiRJkiRpS/xgK0mSJEmSJEmSJElb4gdbSZIkSZIkSZIkSdoSP9hKkiRJkiRJkiRJ0pbEU4c38aODwZWDgLMHCtPhy9kD3+lA4RdffPFnpvWnPffBgwchdnFxEWLL5TLE6H2zB5tnD2PPHvCcPfQ5e1hy9pBmyhdCh0PTIdJ0QHb2MPHsAeh98jRbblTXsodr9ynLPmnOHvRN70Gx7EHpbds+9r1UX7KHk9NzSfbg9ewh633q7u7ubojR+1IsWzfouVn0e5RXejLKsrxSBiX8rVZd5Mq3Kh+/HmxaVebqUFnE+td2sZ1nfy/r3vxuiH3j+p0Q+wDmFt998eUQ+yeHvxEfchn72NOLWYi9sn8Dbo3P3RnEvoT8m4e/F2Lfe/CvQuxffPD9+Fyac81PQuzVvVdC7DujoxCroU6u2jgHuVifh9gM8qBY3g+hncFeiFG9OoD0PUsuVmch9ubZD0Ps7mWsu//33/1vQuzr16+H2M2d/RCbN//fEKOx9itHXw6xl3ZeCrH90WGIPQ2XUIcq6E+HVVyT9Jnzb9rT6P/00w3quhg+Mk9toC10TawvbROvo3VFXcMcGObFN27E9kvWMBc9Oz0NsdnlZYhd4jogvgc9oytgbZpsM9S21kVMy/5oGmLzVbzuYDQOsQfDhyFWNfBcmsvDUuNyHvNvOIh9CZXlrIv3PlzGvYx7FzHNqya+73Idx9BxFfPgYJjsi6HYaJxeLuO+TxGreDGo4nqmorVGmdvqonuzdY3upXUorYkpn88WcZxeNjFf3jr9MMSmVZwPjep5TEsTM7WCe/cncTwfJrcPee0Xn0E7UA3kVQtj6KqJ+UfFNt2J7TzbtkhZwbsl/x0M7t3QMxyTn6q7d+9emZdOp7HO0H4LtfXXXnstxGjOS+M3XffWW2+F2NlZ7CcIpZli9L7ZvaTsflp2f5Puze5XZfe6snva2b3H7Ltlvwlk8yCLyoi+n9B1ffYAqTxIdh85u0dJ5UFtNVtPyWwW91sozX32lgnl6aa/DdE3uEuY41MfRrLlkW0L2e8E9E2K0kK/R+Wb7Xc3yZmAJEmSJEmSJEmSJG2JH2wlSZIkSZIkSZIkaUv8YCtJkiRJkiRJkiRJW+IHW0mSJEmSJEmSJEnaknjS7wY0TXPl0F86AJgOMs4err2zsxNidGgxXUeHa9NhxG+++WaIvf/++yF2cXGR+r2Tk5NUWsbjcYhlD/qmfJ7P5yFG+UwHPNOBzNm00DOyB4JnD5/PprnPIdzZQ8LpOjp0PFvvqT5n6wu9B/0eyR7qTe9Gh45TWyB0eHr2QHWSLV/KF3pG9gB0Kl/KK0LvS22G8pli9L6E0pdtq9l6kM0DPR1VmWtHTRfHlK6AttDFtpB9xtNAbYHa9KZ99fDnQuw/+BLkH+Tp73/4Tojd+PadEDs5jXOQn//2l0LsL73y6yHWdLFd3pt/EGJ1GfuD/9v3/vMQ+2d/+KMQWyxif3Dr9lGI/S++9R+F2Hdv/lqIZQ2rOF52UE/p3dZtzBe693B84zFTt3nHi/shVldxfPs3D/91iP3O3d8NsX/+/h/F2L/4foi9c/t6iI1GcSz7la8eh9gDmJ++cfjjEPvmjTdC7NdfjHVj//AwxLIaKPPj5QOIPQyxST0JsdvT2FazY3Ifiwbm/FB3KS3DEuYgUIfUX9t2V+aQHcwnaS7VtHFMprF2MpmGWA3z3fEk1t3BINaDFczl33333RC7fz/2Q7QOpTkhradHMC8eDWPfTvPTFmLTLu4LHA1ins7qmOaTy5i+ahrztGliWe7vxedenxyFWAFdxLqDdRTMX948eTvE7p2chdgK8n4MffbrRzdD7No49vdtci7VtvG65Sq+2wLqGq23RoMY2xnHfB4OoQ+D/q+uc3PWDt6D8mC2vAwxqhsPLuI4c7o8DbGLJubLxXmsp4sBrd/i+zZQ1yi2KmP72BnEfuMaPJfm2dSXUJ2ke8s6xtYFrDlpDreGMod3i79W/JT6klsnr6COr9aQ5gae/BTWKfrE2dnZlb0YGpNoPMvuS+/t7YXYnTtxrnh8fJx6bnbfl/bi6F6aF05gjrC7uxtitB9JeUB7Xdm9uOxavs/6np5B8xV6Br1Hdv8wWx60F0x9UXZPO7tfmi2P7DefbPoolt0LpnLLfp8g1I5wbgJllN2Hp3k/1SHqc3j8ib+X7SPo21X2uxKlJZsvfdo+1b9sPaA8uHfvXohRvjxpz86OqiRJkiRJkiRJkiR9wfjBVpIkSZIkSZIkSZK2xA+2kiRJkiRJkiRJkrQlfrCVJEmSJEmSJEmSpC3JnYD8GZ2dnV05RJgOBb68vAwxOhSYDkumg4fpYPjpdBpidCj6iy++GGLf/va3Q+zu3bshNpvNQuz09DTE6D0ozXQgPcXokGY6aDl7+DLFKJ/puXSINB3mnJW9l+oVHRyePZCeDrmmGN1L+Ucor7LPHQ6HIdbnEHNKM9UXkj0QnGKEyjxbvqTP4eTZNNPvZdNC70tlRDHqc+igdOr/JpNJKn3ZvKd6QLLlpu2pyliWdRn7l7r8fJRlWcR633SxP9j0+37t8Fsh9tdei2361s73QmwI7aiFtroP48zPHcXnfnD5bogdL49D7J2LeN3bMM8ZT+JzX7h5FNNy82aI/fKtXw+xTZvUOyHWdHEcxLZQPZHp8mNZtzHNDxb3Q+z9y/dC7A9P/ijEfvu9H4bY7d3dEPv5b385xL71wu0Qa2B8+7WXvxlib5++H2J/9oWfDzEqj0EV50N90GhObf/GONbdDu6mvrPtYr5QjJ7bFrl5MdXnEv42l/q6AaRZT8bl5UVxfn7+8f+mOdd8sQixdRPLt13HukHj2xzmjrQmpnXKDeizv/zlL4XYwwcPQmwB73GR3AOYzWOapztxbjudxr59CPPTFsbQg/ooXgftbVg9DLHBIP5eXcd2NIV8vrkX83TRxrxatnEdcHx5HGKXy3hdVcc82BvF/NuF9eXh6FqIlZCn2RlSA+VLfd0Y6h8Z1DHN2TU2adaxT1xBmjtar8IYMG9j3V00sXwvm7gXdLqEtjqM9WpvP9b7fahr1L9cm8a9pdkqpu9otB9iNCbTOEPzU1LBOrRJ7rW0TSyP5SK2hQK2NybjuCYejmK9KuF9Oxi7qa4NoNyovuB+E85M9KQ8fPjwSlvZhXkwob0k2veg8fbGjRshdufOnRA7OzsLsfl8HmL37t0LMdpzz6J9I8qX27fjWoD2KKmeZ/ces/tVdB09t8/eY3ZcobTQ79F1lC/Zvcfs3jehfMmmJfvdIZsH2bRQLFu+dB2lhd4jWw9INi3ZfKYY7SPTnjH1EdlvdZQH1NdRX5L9bpPNlz7t49E12UcewJrm5OQk9YxN8l/YSpIkSZIkSZIkSdKW+MFWkiRJkiRJkiRJkrbED7aSJEmSJEmSJEmStCV+sJUkSZIkSZIkSZKkLYmngW/AxcXFlYPGR6NRuCZ7IDgdbkyHrNOhyoTScvPmzRD7zne+E2L3798PscViEWI/+clPQozeg9JCeUB5RbHs4e4UGw6HIUYHQdMznqUD0LP5l82XbJqzh8pTjPKe3pdk05c9rJveI/vcTV9H+ZIt3+z79inz7O9RmyHUl1C/Qe+7s7MTYtPpNJWWPu9GMcp7qvfZuqbtyZZQ08W+uC5jmT8NbZfrO+m6dbsMsQbeY1SPP3vCPsUvXv+zIbY/PAix/9lX/pMQuzf/IPWM/dFRKvbh7L0Qe3n3lRB785W3Q+zGDvze5cMQ+8uv/HlO5BM2HUA/CbFnXdPBfLKO88nD8WGIffnwSyG2/9W9EPvg4l6IvfatF0Ps1s71GJvGOfUb+/G5P9p/M8T2hjEtO1BG10bxuZs2gederM5CbFDFcbWFPpE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\n",
      "text/plain": [
       "<Figure size 2400x600 with 8 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt_fig = plt.figure(figsize=(12,3), dpi=200)\n",
    "plt_axis = plt_fig.subplots(1, 4)\n",
    "\n",
    "viz.visualize_image_attr(attributions[0,0].cpu().detach().unsqueeze(-1).repeat(1,1,3).numpy(),\n",
    "                         ctimg[0].unsqueeze(-1).repeat(1,1,3).numpy(),\n",
    "                         method=\"original_image\",\n",
    "                         sign=\"all\",\n",
    "                         plt_fig_axis=[plt_fig, plt_axis[0]],\n",
    "                         show_colorbar=True,\n",
    "                         outlier_perc=1,\n",
    "                         use_pyplot=False)\n",
    "viz.visualize_image_attr(attributions[0,0].cpu().detach().unsqueeze(-1).repeat(1,1,3).numpy(),\n",
    "                         ctimg[0].unsqueeze(-1).repeat(1,1,3).numpy(),\n",
    "                         method=\"heat_map\",\n",
    "                         sign=\"positive\",\n",
    "                         plt_fig_axis=[plt_fig, plt_axis[1]],\n",
    "                         show_colorbar=True,\n",
    "                         outlier_perc=1,\n",
    "                         use_pyplot=False)\n",
    "viz.visualize_image_attr(attributions[0,0].cpu().detach().unsqueeze(-1).repeat(1,1,3).numpy(),\n",
    "                         ctimg[0].unsqueeze(-1).repeat(1,1,3).numpy(),\n",
    "                         method='blended_heat_map',\n",
    "                         sign=\"positive\",\n",
    "                         plt_fig_axis=[plt_fig, plt_axis[2]],\n",
    "                         show_colorbar=True,\n",
    "                         outlier_perc=1,\n",
    "                         alpha_overlay=0.3,\n",
    "                         use_pyplot=False)\n",
    "viz.visualize_image_attr(attributions[0,0].cpu().detach().unsqueeze(-1).repeat(1,1,3).numpy(),\n",
    "                         ctaimg[0].unsqueeze(-1).repeat(1,1,3).numpy(),\n",
    "                         method=\"original_image\",\n",
    "                         sign=\"all\",\n",
    "                         plt_fig_axis=[plt_fig, plt_axis[3]],\n",
    "                         show_colorbar=True,\n",
    "                         outlier_perc=1,\n",
    "                         use_pyplot=False)\n",
    "\n",
    "#plt_fig.savefig('a.png', bbox_inches='tight')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4a93b5dc",
   "metadata": {},
   "outputs": [],
   "source": [
    "plt_fig = plt.figure(figsize=(12,3), dpi=200)\n",
    "plt_axis = plt_fig.subplots(1, 4)\n",
    "\n",
    "viz.visualize_image_attr(attributions_ig_nt[0,0].cpu().detach().unsqueeze(-1).repeat(1,1,3).numpy(),\n",
    "                         ctimg[0].unsqueeze(-1).repeat(1,1,3).numpy(),\n",
    "                         method=\"original_image\",\n",
    "                         sign=\"all\",\n",
    "                         plt_fig_axis=[plt_fig, plt_axis[0]],\n",
    "                         show_colorbar=True,\n",
    "                         outlier_perc=1,\n",
    "                         use_pyplot=False)\n",
    "viz.visualize_image_attr(attributions_ig_nt[0,0].cpu().detach().unsqueeze(-1).repeat(1,1,3).numpy(),\n",
    "                         ctimg[0].unsqueeze(-1).repeat(1,1,3).numpy(),\n",
    "                         method=\"heat_map\",\n",
    "                         sign=\"positive\",\n",
    "                         plt_fig_axis=[plt_fig, plt_axis[1]],\n",
    "                         show_colorbar=True,\n",
    "                         outlier_perc=1,\n",
    "                         use_pyplot=False)\n",
    "viz.visualize_image_attr(attributions_ig_nt[0,0].cpu().detach().unsqueeze(-1).repeat(1,1,3).numpy(),\n",
    "                         ctimg[0].unsqueeze(-1).repeat(1,1,3).numpy(),\n",
    "                         method='blended_heat_map',\n",
    "                         sign=\"positive\",\n",
    "                         plt_fig_axis=[plt_fig, plt_axis[2]],\n",
    "                         show_colorbar=True,\n",
    "                         outlier_perc=1,\n",
    "                         alpha_overlay=0.3,\n",
    "                         use_pyplot=False)\n",
    "viz.visualize_image_attr(attributions_ig_nt[0,0].cpu().detach().unsqueeze(-1).repeat(1,1,3).numpy(),\n",
    "                         ctaimg[0].unsqueeze(-1).repeat(1,1,3).numpy(),\n",
    "                         method=\"original_image\",\n",
    "                         sign=\"all\",\n",
    "                         plt_fig_axis=[plt_fig, plt_axis[3]],\n",
    "                         show_colorbar=True,\n",
    "                         outlier_perc=1,\n",
    "                         use_pyplot=False)\n",
    "\n",
    "plt_fig.savefig('a.png', bbox_inches='tight')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f46b077c",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e2022dfb",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "12b00780",
   "metadata": {},
   "outputs": [],
   "source": [
    "ax = plt_fig.add_subplot(2,3,4)\n",
    "ax.imshow((ctaimg[0].unsqueeze(-1).repeat(1,1,3).numpy()*255).astype(np.uint8))\n",
    "ax.axis('off')\n",
    "plt_fig.savefig('a.png', bbox_inches='tight')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9ad55333",
   "metadata": {},
   "outputs": [],
   "source": [
    "noise_tunnel = NoiseTunnel(ig)\n",
    "\n",
    "attributions_ig_nt = noise_tunnel.attribute(input, nt_samples=10, nt_type='smoothgrad_sq', target=pred_label_idx)\n",
    "_ = viz.visualize_image_attr_multiple(np.transpose(attributions_ig_nt.squeeze().cpu().detach().numpy(), (1,2,0)),\n",
    "                                      np.transpose(transformed_img.squeeze().cpu().detach().numpy(), (1,2,0)),\n",
    "                                      [\"original_image\", \"heat_map\"],\n",
    "                                      [\"all\", \"positive\"],\n",
    "                                      cmap=default_cmap,\n",
    "                                      show_colorbar=True)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "138f9055",
   "metadata": {},
   "source": [
    "# 3D模型可视化独立数据集"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "616d331a",
   "metadata": {},
   "outputs": [],
   "source": [
    "from math import sqrt\n",
    "import copy\n",
    "import  traceback\n",
    "import shutil\n",
    "import random\n",
    "import numpy as np  # linear algebra\n",
    "import pydicom\n",
    "import os\n",
    "import matplotlib.pyplot as plt\n",
    "import cv2\n",
    "from pydicom.uid import UID\n",
    "from PIL import Image\n",
    "from tqdm import tqdm\n",
    "import openpyxl\n",
    "\n",
    "import torch\n",
    "import torch.nn as nn\n",
    "import torch.nn.functional as F\n",
    "from torch.utils.data import DataLoader, WeightedRandomSampler, Dataset\n",
    "import torchvision.transforms as T\n",
    "from sklearn import metrics\n",
    "import matplotlib.pyplot as plt\n",
    "from data.datasets import AortaDataset3DCenter\n",
    "from model.resnet3d import resnet3d\n",
    "import data.transforms as MT\n",
    "\n",
    "import time\n",
    "\n",
    "from captum.attr import IntegratedGradients,DeepLiftShap,DeepLift\n",
    "from captum.attr import GradientShap\n",
    "from captum.attr import LRP\n",
    "from captum.attr import Occlusion\n",
    "from captum.attr import NoiseTunnel\n",
    "from captum.attr import visualization as viz\n",
    "from captum.attr._utils.lrp_rules import EpsilonRule, GammaRule, Alpha1_Beta0_Rule\n",
    "\n",
    "import re\n",
    "from os import listdir\n",
    "from os.path import join, isdir, isfile, exists"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "92a089fa",
   "metadata": {},
   "outputs": [],
   "source": [
    "class AortaDataset3DCenterTest(Dataset):\n",
    "    def __init__(self, img_dir, cate, transform, depth, step=1, residual=False, supcon=False, mask_dir=None):\n",
    "        self.img_dir = img_dir\n",
    "        self.transform = transform\n",
    "        assert depth % 2 == 1, 'depth should be odd number.'\n",
    "        self.depth = depth\n",
    "        self.step = step\n",
    "        self.residual = residual\n",
    "        self.supcon = supcon\n",
    "        self.mask_dir = mask_dir\n",
    "        assert not (mask_dir is None and residual == True)\n",
    "        cate = [str(i) for i in cate]\n",
    "        self.labels = sorted([label for label in listdir(img_dir) if isdir(join(img_dir, label)) and label in cate])\n",
    "        self.datas = []\n",
    "        for i, label in enumerate(self.labels):\n",
    "            img_list = sorted(list(filter(lambda x: not x.startswith('.') and isfile(join(img_dir, label, x)), listdir(join(img_dir, label)))))\n",
    "            il_len = len(img_list)\n",
    "            for j in range(il_len):\n",
    "                nl = re.split('[_.]', img_list[j])\n",
    "                assert len(nl) == 4 or len(nl) == 5, f'Format of image file name \"{img_list[j]}\" is wrong.'\n",
    "                if len(nl) == 5:\n",
    "                    continue\n",
    "                group_list = [img_list[j]]\n",
    "                half = s = depth // 2\n",
    "                for k in range(j-1, j-step*(half)-1, -1):\n",
    "                    if k < 0 or k >= il_len:\n",
    "                        for _ in range(s):\n",
    "                            group_list.insert(0, group_list[0])\n",
    "                        break\n",
    "                    nlk = re.split('[_.]', img_list[k])\n",
    "                    if nl[0] != nlk[0] or nl[1] != nlk[1]:\n",
    "                        for _ in range(s):\n",
    "                            group_list.insert(0, group_list[0])\n",
    "                        break\n",
    "                    offset = int(nl[2]) - int(nlk[2])\n",
    "                    if offset % step == 0:\n",
    "                        for _ in range(offset//step-(half-s)):\n",
    "                            group_list.insert(0, img_list[k])\n",
    "                            s -= 1\n",
    "                            if s == 0:\n",
    "                                break\n",
    "                    if s == 0:\n",
    "                        break\n",
    "                s = depth // 2\n",
    "                for k in range(j+1, j+step*(half)+1):\n",
    "                    if k < 0 or k >= il_len:\n",
    "                        for _ in range(s):\n",
    "                            group_list.insert(0, group_list[0])\n",
    "                        break\n",
    "                    nlk = re.split('[_.]', img_list[k])\n",
    "                    if nl[0] != nlk[0] or nl[1] != nlk[1]:\n",
    "                        for _ in range(s):\n",
    "                            group_list.append(group_list[-1])\n",
    "                        break\n",
    "                    offset = int(nlk[2]) - int(nl[2])\n",
    "                    if offset % step == 0:\n",
    "                        for _ in range(offset//step-(half-s)):\n",
    "                            group_list.append(img_list[k])\n",
    "                            s -= 1\n",
    "                            if s == 0:\n",
    "                                break\n",
    "                    if s == 0:\n",
    "                        break\n",
    "                assert len(group_list) == depth, f'depth wrong: {img_list[j]}'\n",
    "                if mask_dir is None:\n",
    "                    self.datas.append([[join(img_dir, label, img) for img in group_list], i])\n",
    "                else:\n",
    "                    group_mask_list = [join(mask_dir, img) if exists(join(mask_dir, img)) else None for img in group_list]\n",
    "                    self.datas.append([[join(img_dir, label, img) for img in group_list], i, group_mask_list])\n",
    "\n",
    "    def __len__(self):\n",
    "        return len(self.datas)\n",
    "\n",
    "    def __getitem__(self, index):\n",
    "        label = torch.tensor(self.datas[index][1], dtype=torch.long)\n",
    "        img_list = []\n",
    "        for img_path in self.datas[index][0]:\n",
    "            img_list.append(Image.open(img_path))\n",
    "        if self.supcon:\n",
    "            img_list1 = self.transform(img_list)\n",
    "            if self.residual:\n",
    "                for i in range(1, len(img_list1)):\n",
    "                    res = img_list1[i] - img_list1[i-1]\n",
    "                    res = (res + 1) / 2\n",
    "                    img_list1.append(res)\n",
    "            imgs1 = torch.stack(img_list1, dim=1)\n",
    "            img_list2 = self.transform(img_list)\n",
    "            if self.residual:\n",
    "                for i in range(1, len(img_list2)):\n",
    "                    res = img_list2[i] - img_list2[i-1]\n",
    "                    res = (res + 1) / 2\n",
    "                    img_list2.append(res)\n",
    "            imgs2 = torch.stack(img_list2, dim=1)\n",
    "            return [imgs1, imgs2], label\n",
    "        elif self.mask_dir is None:\n",
    "            img_list = self.transform(img_list)\n",
    "            if self.residual:\n",
    "                for i in range(1, len(img_list)):\n",
    "                    res = img_list[i] - img_list[i-1]\n",
    "                    res = (res + 1) / 2\n",
    "                    img_list.append(res)\n",
    "            imgs = torch.stack(img_list, dim=1)\n",
    "            centerimg = self.datas[index][0][len(self.datas[index][0])//2]\n",
    "            return imgs, label, centerimg\n",
    "        else:\n",
    "            mask_list = []\n",
    "            for i, mask_path in enumerate(self.datas[index][2]):\n",
    "                if mask_path is None:\n",
    "                    mask = Image.fromarray(np.ones((img_list[i].height, img_list[i].width), dtype=np.uint8)*255)\n",
    "                else:\n",
    "                    mask = Image.open(mask_path)\n",
    "                mask_list.append(mask)\n",
    "            cat_list = img_list + mask_list\n",
    "            cat_list = self.transform(cat_list)\n",
    "            img_list, mask_list = cat_list[:self.depth], cat_list[self.depth:]\n",
    "            imgs, masks = torch.stack(img_list, dim=1), torch.stack(mask_list, dim=1)\n",
    "            return imgs, label, masks"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "7e0b0980",
   "metadata": {},
   "outputs": [],
   "source": [
    "vt_list = [\n",
    "    MT.Resize3D(81),\n",
    "    MT.CenterCrop3D(81),\n",
    "    MT.ToTensor3D(),\n",
    "    MT.SobelChannel(3, flag_3d=True)\n",
    "]\n",
    "val_transform = T.Compose(vt_list)\n",
    "val_dataset = AortaDataset3DCenterTest(\n",
    "    '/medical-data/zsxm/AorticDissection/adpaper/5independent/ct/3dcenter',\n",
    "    cate=[0,1], transform=val_transform, depth=7, step=1)\n",
    "val_loader = DataLoader(val_dataset,\n",
    "                        batch_size=128,\n",
    "                        shuffle=False,\n",
    "                        drop_last=False,\n",
    "                        num_workers=8, \n",
    "                        pin_memory=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "08aa383a",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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